Device and method for clinical evaluation

ABSTRACT

Systems and methods of evaluating a patient comprise the steps of obtaining temporal data of the patient after an event, monitoring a plurality of patient parameters, compiling patient data based on the temporal data and patient parameters, determining a state or change in state of the patient based on the compiled patient data, and alerting medical staff of the state or change in state.

REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/858,898, filed Jun. 7, 2019, entitled “DEVICE AND METHOD FOR CLINICALEVALUATION,” and hereby specifically and entirely incorporated byreference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The invention is a system and a method that diagnoses and/or stratifiespatients by taking in data from various physiological parameters,demographics, clinical events, treatments, lab test results usingsupervised and unsupervised learning techniques including but notlimited to machine learning, Bayesian inference networks, expertsystems, principal component analysis, fractal analysis, statisticaltechniques, heuristic analysis, deep temporal continuous anddiscontinuous clustering that integrates dimensionality reduction andtemporal clustering into a single end-to-end learning framework, maximummargin temporal clustering, fuzzy logic, and neural networks.

2. Description of the Background

Physiological monitors provide information that can be used by doctorsto directly diagnose patients or as part of an integrated approach togenerate physiological scores that indicate patient deterioration.Physiological scores, such as the Mortality Probability Model (MPM), theAcute Physiology and Chronic Health Education (APACHE), the SimplifiedAcute Physiological Score (SAPS) and the Therapeutic InterventionScoring System (TISS) have shown significant improvements in patientoutcomes. Monitoring sick patients by using physiological scores andvital signs in their early stages of illness, even prior to organfailure or shock, improves outcomes. Close monitoring of patients allowsfor recognition of patient degeneration and the administration of theappropriate therapy.

Early Warning Systems

An array of early warning scores (EWS) are used by doctors to warn ofimpending patient deterioration including the modified early warningscore (MEWS), pediatric early warning score (PEWS), modified earlyobstetric warning score (MEOWS), and national early warning score(NEWS). Implementing EWS as part of a protocol for patient care hasimproved overall outcomes, however, these methods do not accuratelypredict patient outcomes in approximately 15% of ICU patients.Furthermore, the sensitivity and specificity of such scores may be worsefor patients in a respiratory intensive care unit, which provide care inhospitals with large number of patients with acute respiratory failure.This may be in part due to the limited predictive power of respiratoryrate in early diagnosis of respiratory complications. It has been shownthat the rate of breathing has poor sensitivity and specificity ofpredicting low minute ventilation, which is the product of tidal volumeand respiratory rate and is the fundamental measure of ventilation.Until recently minute ventilation has been difficult to measure,particularly in non-intubated patients.

While EWS present a general warning of patient deterioration, otherscores are used to aid or confirm a more specific diagnosis, such assepsis. Several scores have been used as part of sepsis protocolsincluding the Systemic Inflammatory Response Syndrome (SIRS), SequentialOrgan Failure Assessment (SOFA), and its more feasible counterpartcalled quick SOFA (qSOFA). These scores take into account variousphysiological parameters available from monitoring devices or clinicalobservation (temperature, heart rate, respiratory rate, blood pressure,mental status, suspicion of infection), and lab tests (white blood cellcount, platelet count, bilirubin, creatinine). Notably, these scoresonly consider respiratory rate, while ignoring tidal volume, despite thecorrelation between ventilation and increased metabolic demandexperienced in sepsis.

Tradeoffs between accuracy, timeliness, and feasibility have been madein selecting parameters to calculate such scores. While lab tests canprovide information to increase accuracy of scores, they are not alwaystimely or feasible. More importantly the reporting frequency ofphysiological parameters influence the timeliness with which scores canbe calculated. For example, vital signs may only be taken every 4 hourson patients on the hospital floor, thus the physiological score can onlybe updated at the same frequency. Even in the ICU, only some patientundergo continuous monitoring that lends itself to more frequent scorecalculation. Furthermore, continuous monitoring has been shown to haveissues with false alarms and both patient and staff compliance.

Monitoring Ventilation

Previously, direct monitoring of ventilation was limited to intubatedpatients confined to the ICU or OR. Non-invasive continuous monitoringof minute ventilation has recently been made feasible by advances in abio-impedance based Respiratory Volume Monitor (RVM). By providingcontinuous, real-time measurements of not only rate of breathing, butalso depth of breathing, ventilation measurements may be incorporatedinto new physiological scores and warning systems for predicting adverseoutcomes such as respiratory depression, respiratory failure, sepsis,congestive heart failure, COPD exacerbations, asthma attacks, etc. Thelow false alarm rate associated with the RVM allows for continuousmonitoring and more frequent calculation of physiological scoresutilizing respiratory metrics, particularly tidal volume and minuteventilation. Earlier methods of monitoring ventilation, including pulseoximetry, capnography, and respiratory rate counting, have been shown tobe delayed indicators of respiratory compromise, and furthermore havebeen prone to false alarms.

Shortcomings of Current Monitoring Methods—False Alarms

Current monitoring methods can alert medical staff of patientdeterioration, but are also prone to false alarms, which limit theefficacy of monitoring equipment. In fact, the vast majority of alarms(87%) reported by monitors in the ICU are false positive alarms.Excessive alarms result in alarm fatigue for medical staff, which canlead to behavior that decreases patient safety. For example, medicalprofessionals may ignore alarms in an attempt to decrease alarm fatigue.It has been reported that less than 1% of alarms lead to changes inpatient care. Likewise, 78% of clinicians disable alarms entirely, whichplaces patients at greater risk of a missed event.

The problem of alarm fatigue has been recognized by the JointCommission, which made improved alarm management a goal in its safetyinitiative. Despite the new emphasis on alarms, programs to improvealarm safety have had little success and alarm fatigue persists.

Part of the problem of excessive alarms are the thresholds used tosignal an alarm event. Studies have shown that decreasing alarmthresholds can result in a decrease of false alarms. For example, pulseoximetry can alarm for hypoxia at a threshold of 90%, but significantlylower false alarm rates were measured using 85% or 80% as thresholds.While a lower threshold may result in fewer false positive alarms, itincreases the chance of false negatives. Given that hypoxia is already alate indicator of changes in ventilation, further delays in alarmscaused by lower thresholds can put patients at greater risk ofundetected respiratory compromise.

The majority of physiologic monitors are designed to alarm based onalarm thresholds, and may have no indication of the quality of thesignal used to trigger the alarm or the context of the alarm. Despitemost alarms being false positives, monitors have no ability to qualifyalarms after they have occurred and sounded. Therefore, it may be usefulto have a system to identify alarms from physiologic monitors and labelthem as “true” or “false” positives, or conversely, as “false” negativesin the absence of alarms that occurred during a missed event. Such asystem could decrease alarm fatigue by either cancelling alarms orlimiting the frequency with which medical staff are required to respondto alarms deemed to be false positives, either in real time orretrospectively. Furthermore, the system could improve early warningsystems and diagnosis algorithms by qualifying alarms with theappropriate contextual data including: physiologic data from othermonitors, temporal information regarding events and spatial informationabout the patient.

Yet another concern with physiologic monitoring and early warningsystems has been the lack of temporal context to the measurements used.For example, the accuracy of the physiologic measurements is known tochange with time, but this is generally not considered in the thresholdsused to detect alarms.

Physiological Monitoring—History and Evolution

Patient monitoring is essential because it provides warning to patientdeterioration and allows for the opportunity of early intervention,greatly improving patient outcomes. For example, modern monitoringdevices can detect abnormal heart rhythms, blood oxygen saturation, andbody temperature, which can alert clinicians of a deterioration thatwould otherwise go unnoticed.

The earliest records of patient monitoring reveal that ancient Egyptianswere aware of the correlation between peripheral pulse and the heartbeat as early as 1550 BC. Three millennia passed before the nextsignificant advancement in monitoring was made, with Galileo using apendulum to measure pulse rate. In 1887, Waller determined that he couldpassively record electrical activity across the chest by usingelectrodes and correlated the signal to activity from the heart.Waller's discovery paved the way for the use of electrical signals as amethod to measure physiological signals. However, it would still taketime before scientists recognized the advantages of monitoring aphysiological signal in a clinical environment.

In 1925, MacKenzie emphasized the importance of continuous recording andmonitoring of physiological signals such as the pulse rate and bloodpressure. He specifically stressed that the graphical representation ofthese signals is important in the assessment of a patient's condition.In the 1960s, with the advent of computers, patient monitors improvedwith the addition of a real-time graphical display of multiple vitalsigns being recorded simultaneously. Alarms were also incorporated intomonitors and were triggered when signals, such as a pulse rate or bloodpressure, reached a certain threshold.

The first patient monitors were used on patients during surgery. Aspatient outcomes were shown to improve, monitoring of vital signs spreadto other areas of the hospital such as the intensive care unit and theemergency department. For instance, pulse oximetry was first widely usedin operating rooms as a method to continuously measure a patient'soxygenation non-invasively. Pulse oximetry quickly became the standardof care for the administration of general anesthetic and subsequentlyspread to other parts of the hospital, including the recovery room andintensive care units.

The Growing Need for Improved Patient Monitoring

The number of critically ill patients presenting to the emergencydepartment is increasing at a great rate, and these patients requireclose monitoring. It has been estimated that between 1-8% of patients inthe emergency department require a critical care procedure to beperformed, such as a cardiovascular procedure or a thoracic andrespiratory procedure (mechanical ventilation, catheter insertion,arterial cannulation).

Physiological scores, such as the Mortality Probability Model (MPM), theAcute Physiology and Chronic Health Education (APACHE), the SimplifiedAcute Physiological Score (SAPS) and the Therapeutic InterventionScoring System (TISS) have shown significant improvements in patientoutcomes. Monitoring sick patients by using physiological scores andvital signs in their early stages of illness, even prior to organfailure or shock, improves outcomes. Close monitoring of patients allowsfor recognition of patient degeneration and the administration of theappropriate therapy.

However, current scoring methods do not accurately predict patientoutcomes in approximately 15% of ICU patients, and it may be worse forpatients in a respiratory intensive care unit, which provide care inhospitals with large number of patients with acute respiratory failure.Furthermore, differences in currently monitored vital signs such asblood oxygenation occur late in the progression of respiratory orcirculatory compromise. Often the earliest sign of patient degradationis a change in a patient's breathing effort or respiratory pattern.

Respiratory rate is recognized as a vital indicator of patient healthand is used to assess patient status. However, respiratory rate alonefails to indicate important physiological changes, such as changes inrespiratory volumes. Metrics derived from continuous volume measurementshave been shown to have great potential for determining patient statusin a wide range of clinical applications. However, there are currentlyno adequate systems that can accurately and conveniently determinerespiratory volumes, which motivates the need for a non-invasiverespiratory monitor that can trace changes in breath volume.

Shortcomings of Current Methods

Currently, a patient's respiratory status is monitored with methods suchas spirometry and end tidal CO₂ measurements. These methods are ofteninconvenient to use and inaccurate. While end tidal CO₂ monitoring isuseful during anesthesia and in the evaluation of intubated patients ina variety of environments, it is inaccurate for non-ventilated patients.The spirometer and pneumotachometer are limited in their measurementsare highly dependent on patient effort and proper coaching by theclinician. Effective training and quality assurance are a necessity forsuccessful spirometry. However, these two prerequisites are notnecessarily enforced in clinical practice like they are in researchstudies and pulmonary function labs. Therefore quality assurance isessential to prevent misleading results.

Spirometry is the most commonly performed pulmonary function test. Thespirometer and pneumotachometer can give a direct measurement ofrespiratory volume. It involves assessing a patient's breathing patternsby measuring the volume or the flow of air as it enters and leaves thepatient's body. Spirometry procedures and maneuvers are standardized bythe American Thoracic Society (ATS) and the European Respiratory Society(ERS). Spirometry can provide important metrics for evaluatingrespiratory health and diagnosing respiratory pathologies. The majordrawback of mainstream spirometers is that they require the patient tobreathe through a tube so that the volume and/or flow rate of his breathcan be measured. Breathing through the apparatus introduces resistanceto the flow of breath and changes the patient's breathing pattern. Thusit is impossible to use these devices to accurately measure a patient'snormal breathing. Breathing through the apparatus requires a conscious,compliant patient. Also, in order to record the metrics suggested by theATS and ERS, patients must undergo taxing breathing maneuvers, whichexcludes most elderly, neonatal, and COPD patients from being able toundergo such an examination. The outcomes of the procedures are alsohighly variable dependent on patient effort and coaching, and operatorskill and experience. The ATS also recommends extensive training forhealthcare professionals who practice spirometry. Also, many physiciansdo not have the skills needed to accurately interpret the data gainedfrom pulmonary function tests. According to the American ThoracicSociety, the largest source of intrasubject variability is improperperformance of test. Therefore much of the intrapatient and interpatientvariability in pulmonary function testing is produced by human error.Impedance-based respiratory monitoring fills an important void becausecurrent spirometry measurements are unable to provide continuousmeasurements because of the requirement for patient cooperation andbreathing through a tube. Therefore there is a need for a device thatprovides near-real-time information over extended periods of time (vs.spirometry tests which last a minute or less) in non-intubated patientsthat can show changes in respiration related to a provocative test ortherapeutic intervention.

In order to acquire acceptable spirometry measurements, as dictated byATS standards, healthcare professionals must have extensive training andtake refresher courses. A group showed that the amount of acceptablespirometry measurements was significantly greater for those who did atraining workshop (41% vs. 17%). Even with acceptable spirometrymeasurements, the interpretations of the data by primary physicians weredeemed as incorrect 50% of the time by pulmonologists. However, it wasnoted that aid from computer algorithms showed improvement ininterpreting spirograms when adequate spirometry measurements werecollected.

Rigorous training is needed for primary care clinics to acquireacceptable spirometry measurements and make accurate interpretations.However, resources to train a large number of people and enforcesatisfactory quality assurance are unreasonable and inefficient. Even ina dedicated research setting, technician performance falls over time.

In addition to human error due to the patient and healthcare provider,spirometry contains systematic errors that ruin breathing variabilitymeasurements. Useful measurements of breath by breath patterns andvariability have been shown to be compounded by airway attachments suchas a facemask or mouthpiece. Also, the discomfort and inconvenienceinvolved during measurement with these devices prevents them from beingused for routine measurements or as long-term monitors. Other lessintrusive techniques such as thermistors or strain gauges have been usedto predict changes in volume, but these methods provide poor informationon respiratory volume. Respiratory belts have also shown promise inmeasuring respiration volume, but groups have shown that they are lessaccurate and have a greater variability than measurements from impedancepneumography. Therefore, a system that can measure volume for longperiods of time with minimal patient and clinician interaction isneeded.

Pulmonary Function Testing and Preoperative, Postoperative Care

Preoperative care is centered on identifying what patientcharacteristics may put the patient at risk during an operation andminimizing those risks. Medical history, smoking history, age, and otherparameters dictate the steps taken in preoperative care. Specifically,elderly patients and patients with pulmonary diseases may be at risk forrespiratory complications when placed under a ventilator for surgery. Inorder to clear these patients for surgery, pulmonary function tests suchas spirometry are performed which give the more information to determinewhether the patient can utilize the ventilator. Chest x-rays may also betaken. However, these tests cannot be replicated mid-surgery, or innarcotized patients or those who cannot or will not cooperate. Testingmay be uncomfortable in a postoperative setting and disruptive topatient recovery. End Tidal CO₂ and Patient Monitoring

End tidal CO₂ is another useful metric for determining pulmonary stateof a patient. The value is presented as a percentage or partial pressureand is measured continuously using a capnograph monitor, which may becoupled with other patient monitoring devices. These instruments producea capnogram, which represents a waveform of CO₂ concentration.Capnography compares carbon dioxide concentrations within expired airand arterial blood. The capnogram is then analyzed to diagnose problemswith respiration such as hyperventilation and hypoventilation. Trends inend tidal CO₂ are particularly useful for evaluating ventilatorperformance and identifying drug activity, technical problems withintubation, and airway obstruction. The American Society ofAnesthesiologists (ASA) mandates that end-tidal CO₂ be monitored anytime an endotracheal tube or laryngeal mask is used, and is also highlyencouraged for any treatment that involves general anesthesia.Capnography has also been proven to be more useful than pulse oximetryfor monitoring of patient ventilation. Unfortunately, it is generallyinaccurate and difficult to implement in the non-ventilated patient, andother complementary respiratory monitoring methods would have greatutility.

Echocardiograms

Fenichel et al. determined that respiratory motion can causeinterference with echocardiograms if it is not controlled for.Respiratory motion can block anterior echoes through pulmonary expansionand it chances the angle of incidence of the transducer ray relative tothe heart. These effects on the echocardiography signal can decrease theaccuracy of measurements recorded or inferred from echocardiograms.Combining echocardiography with accurate measurement of the respiratorycycle can allow an imaging device to compensate for respiratory motion.

Impedance Pneumography

Impedance pneumography is a simple method that can yield respiratoryvolume tracings without impeding airflow, does not require contact withthe airstream, and does not restrict body movements. Furthermore, it maybe able to make measurements that reflect functional residual capacityof the lungs.

While attempting to measure cardiac activity, Atzler and Lehmann notedtransthoracic electrical impedance changed with respiration. Theyregarded the respiratory impedance changes as artifacts and asked thepatients to stop breathing while measurements were made. In 1940, whilealso studying cardiac impedance, Nyboer noticed the same respiratoryimpedance artifact in his measurement. He confirmed the origin of theartifact by being the first person to relate changes in transthoracicimpedance to changes in volume using a spirometer by simultaneouslyrecording both. Goldensohn and Zablow took impedance pneumography a stepfurther by being the first investigators to quantitatively relaterespired volume and transthoracic impedance. They reported difficulty inseparating the cardiac signal artifacts and also noted artifacts duringbody movements. However, after comparing the impedance changes andrespired volume changes by a least squares regression, they importantlydetermined that the two are linearly related. Other groups haveconfirmed the linear relationship between transthoracic impedancechanges and respiratory breaths and have found that approximately 90% ofthe spirometric signal can be explained by the thoracic impedancesignal. While the relationship has been shown to be linear, many groupsfound the calibration constants for intrapatient and interpatient to behighly variable between trials. These differences in calibrationconstants can be attributed to a variety of physiological and electrodecharacteristics, which must be taken into account.

Transthoracic Impedance Theory

Electrical impedance is a complex quantity defined as the sum of theresistance (R), the real component, and the reactance (X), the imaginarycomponent (Z=R+jX=−Z−^(jΘ)). It is used as the measurement of oppositionto an alternating current. Mathematically, impedance is measured by thefollowing equation, which is analogous to Ohm's law:

Z=V/I (1)

Where voltage=V, current=I, and impedance=Z. An object that conductselectricity with unknown impedance can be determined from a simplecircuit. Applying a known alternating current across the object whilesimultaneously measuring the voltage across it and using equation (1)yields the impedance. The thorax represents a volume conductor, andbecause of that, the laws governing ionic conductors can be applied. Inaddition, the movement of organs and the enlargement of the thoraciccage during breathing create a change in conductivity, which can bemeasured. Impedance across the thorax can be measured by introducing aknown current and measuring the change in voltage across the thorax withelectrodes.

Origins of the Transthoracic Impedance Signal

The tissue layers that makeup the thorax and the abdomen, all influencethe measurement of transthoracic impedance. Each tissue has a differentconductivity that influences the direction of current flow betweenelectrodes. Beginning with the outermost layer, the surface of the bodyis covered by skin, which presents a high resistivity but is only about1 mm thick. Under the skin is a layer of fat, which also has a highresistivity. However, the thickness of this layer is highly variable anddepends on body location and the body type of the subject. Movingposterior to anterior, below the layer of skin and fat are the posturalmuscles, which are anisotropic. They have a low resistivity in thelongitudinal direction but a high resistivity in all other directions,which leads to a tendency to conduct current in a direction that isparallel to the skin. Below the muscle are the ribs, which, as bone, arehighly insulating. Therefore, current through the thorax can only flowbetween bones. Once current reaches the lungs, it is hypothesized thatcurrent travels through the blood, which has one of the lowestresistances of any body tissue. Aeration of the lungs changes the sizeof the lung and the pathway of current flow, and manifests itself as achange in resistance or impedance that can be measured.

Due to the anisotropic properties of the tissues, radial current flowthrough the chest is much less than would be expected. Much of thecurrent goes around the chest rather than through it. As a result,impedance changes come from changes in thoracic circumference, changesin lung size, and movement of the diaphragm-liver mass. Measurements atlower thoracic levels are attributed to movement of the diaphragm andliver, and at higher thoracic levels they are attributed to aeration andexpansion of the lungs. Therefore, the impedance signal is the sum ofthe change from the expansion and aeration of the lungs and the movementof the diaphragm-liver mass. Both the abdominal and thoracic componentsare needed in order to observe a normal respiratory signal. In addition,the different origins of impedance changes in the upper and lower thoraxcould explain why greater linearity is observed at higher thoraciclevels.

Influences of Electrode Placement

Transthoracic impedance is measured with electrodes attached to thepatient's skin. Geddes et al. determined that the electrode stimulationfrequency should not be below 20 kHz because of physiological tissueconsiderations. It is a matter of safety and eliminating interferencefrom bioelectric events. In addition, impedance measurements of asubject were found to differ depending on subject position, includingsitting, supine, and standing. It was shown that for a given change involume, laying supine yielded the greatest signal amplitude and lowestsignal to noise during respiration.

Another potential signal artifact comes from subject movements, whichmay move electrodes and disturb calibrations. Furthermore, electrodemovements may be more prevalent in obese and elderly patients, which mayrequire repetitive lead recalibration during periods of long-termmonitoring. Because of the calibration variability between trials, somehave suggested that calibration should be performed for each individualfor a given subject posture and electrode placement. However, a groupwas able to show that careful intrapatient electrode placement canreduce impedance differences between measurements to around 1%.

Despite having the same electrode placements, calibration constants andsignal amplitudes for individuals of different sizes showed variability.It was determined that the change in impedance for a given change involume is the largest for thin-chested people and smaller for people whoare more amply sized. These observed differences may be due to thegreater amount of resistive tissue, such as adipose tissue and muscle,between the electrodes and lungs in larger subjects, yielding an overallsmaller percent change in impedance for a given change in volume forlarger subjects. On the other hand, it is noticeable that in childrenthe cardiac component of the impedance trace is greater than in adults.This may be due to greater fat deposition around the heart in adultsthan in children, which serves to shield the heart from beingincorporated into the impedance measurement.

Electrodes attached to the mid-axillary line at the level of the sixthrib yielded the maximum impedance change during respiration. However,the greatest linearity between the two variables was attained by placingthe electrodes higher on the thorax. Despite the high degree oflinearity reported, large standard deviations of impedance changesduring respiration have been reported. However, the variability observedin impedance measurements is comparable to those seen in measurements ofother vital signs, such as blood pressure. Groups have shown thatimpedance pneumography methods are sufficiently accurate for clinicalpurposes. Furthermore, in the 40 years since these studies, electrodematerials and signal processing of the impedance measurements havegreatly improved, yielding even more reliable measurements. Digitalsignal processing allows for the near instantaneous filtering andsmoothing of real-time impedance measurements, which allows for theminimization of artifacts and noise. More recently, respiratoryimpedance has been used successfully in long-term patient monitoring. Aslong as the electrodes remain relatively unmoved, the relationship ofchange in impedance to change in volume is stable for long periods oftime.

Active Acoustic System

The most common use of acoustics in relationship to the lungs is toevaluate sounds that originate in the lungs acquired by the use of astethoscope. One frequently overlooked property of lung tissue is itsability to act as an acoustic filter. It attenuates various frequenciesof sound that pass through them to different extents. There is arelationship between the level of attenuation and the amount of air inthe lungs. Motion of the chest wall also results in frequency shift ofacoustic signals passing through the thorax.

Potential for Detecting Abnormalities

Many useful indicators, such as the forced vital capacity (FVC) andforced expiratory volume in one second (FEV₁), can be extracted frommonitoring the volume trace of a patient's respiration with impedancepneumography. The FVC and FEV1 are two benchmark indicators typicallymeasured by a spirometer and are used to diagnose and monitor diseasessuch as COPD, asthma, and emphysema. In addition to monitoring therespiration, impedance pneumography can also simultaneously record theelectrocardiogram from the same electrodes.

Breath-to-Breath Variability

Calculations such as breath to breath variability, coefficient ofvariance, standard deviation, and symmetry of a tidal volume histogramhave been shown to be dependent on age and respiratory health. Comparedto normal subjects it has been shown that some of these parameters,particularly coefficient of variance, are significantly different inpatients with tuberculosis, pneumonitis, emphysema, and asthma.Furthermore, it has been noted in the literature that impedancemeasurements were satisfactory as long as the electrodes did not move onthe patient . In general, it has been determined by many groups thathealthy subjects show greater variability in breathing patterns thansubjects in a pulmonary disease state.

The nonlinear analysis of respiratory waveforms has been used in a widearray of applications. In examining the regularity of nonlinear,physiologic data, studies have shown that within pulmonary diseasestates, patients exhibit a decrease in breath-to-breath complexity. Thisdecrease in complexity has been demonstrated within chronic obstructivepulmonary disease, restrictive lung disease, and within patients thatfail extubation from mechanical ventilation. Reduced variability hasalso been determined to be a result of sedation and analgesia. In broadterms, normal patients have greater breath to breath variability thanthose afflicted by some form of pulmonary disease or compromise.

The respiratory pattern is nonlinear, like any physiologic data, as itis influenced by a multitude of regulatory agents within the body.Within the analysis of breath-to-breath variability, various entropymetrics are used to measure the amount of irregularity andreproducibility within the signal. These metrics can be used within theanalysis of RVM tidal volume tracings in assessing not onlybreath-to-breath changes, but intrabreath variability, as well asmagnitude, periodicity, and spatial location of the curve.

Universal calibration of the system based off standardized patientcharacteristic data (Crapo) allows for the creation of a complexityindex, and comparison of a single patient to what is defined as a normallevel of complexity. This index would be used to aid clinicians indetermining the appropriate time to extubate, determining the severityof cardiopulmonary disease, and also within the assessment oftherapeutics. This index would be independent of the method in whichdata is collected, whether through an impedance based device,accelerometers, a ventilator, or an imaging device. The system couldalso be calibrated to a specific patient and focus on intra-subjectvariability while detecting rapid changes within any of the respiratoryparameters.

Nonlinear Analysis of Interbreath Intervals

In addition to variability metrics, some groups have found thatnonlinear analysis of instantaneous interbreath intervals are highlycorrelated to the success of weaning from a mechanical ventilator. Thesemetrics are useful indicators of pulmonary health and can assist inclinical decisions. The inability for a patient to separate from amechanical ventilator occurs in approximately 20% of patientsand currentmethods to predict successful separation are poor and add little to aphysician's decision. In a study with 33 subjects under mechanicalventilation for greater than 24 hours, it was found that 24 subjectswere successfully weaned from ventilation while 8 subjects failed (datafrom one subject was removed). The reasons of failure were cited ashypoxia in five subjects, and tachypnea, hypercapnia, and upper airwayedema for the remaining three, all of which are diseases that can bepotentially identified by an impedance pneumography system. The primaryfinding in this study was that the nonlinear analysis of instantaneousbreath intervals for those who failed to separate from the mechanicalventilator was significantly more regular than those who separatedsuccessfully. Furthermore, it was shown that the respiratory rate didnot differ between the two groups. The metrics derived from nonlinearanalysis of impedance pneumography measurements can successfully predictpatient outcomes. In addition, these metrics have been shown to berobust and did not significantly change when artifacts such as coughingwere introduced.

Detection of Decreased Ventilation States

The respiratory trace produced by impedance pneumography as well as theaverage impedance of a subject can indicate states of decreasedventilation or changes in fluid volume in the thorax. This type ofmonitoring would be useful for the care of anesthetized patients.Respiratory monitoring with impedance pneumography in anaesthetized orimmobile patients is shown to be accurate and reliable for long periods,especially during the critical period in the recovery room after surgery. Investigators have determined that fluid in the thorax or lungs canlead to measurable changes in impedance, which can be used to determinecommon problems for patients in the recovery room such as pulmonaryedema or pneumonia.

In addition to measuring changes in fluid volume in the thorax, changesin tidal volume and upper airway resistance are immediately apparent inimpedance measurements. Investigators found that endotracheal clampingof anaesthetized patients still produced a diminished impedance signaldespite the patient's effort to breathe, thereby giving a correctindication of ventilation. It has also been shown that impedancemeasurements provide quantitative assessment of the ventilation of eachlung. Differences in impedance measurements were observed in patientswith unilateral pulmonary lesions, with a pair of electrodes on theinjured side of the thorax producing a less pronounced signal than thenormal side.

Respiratory Monitors

While certain contact probes record respiratory rate, to date, no deviceor method has been specifically devised to record or to analyzerespiratory patterns or variability, to correlate respiratory patternsor variability with physiologic condition or viability, or to userespiratory patterns or variability to predict impending collapse. Heartrate variability algorithms only report on variations in heart rate,beat-to-beat. It is desirable to use respiratory rate variabilityalgorithms to incorporate variability in respiratory intensity, rate,and location of respiratory motion. Marked abnormalities in respirationas noted by changes in intensity, in rate, in localization ofrespiratory effort, or in variability of any of these parameters providean early warning of respiratory or cardiovascular failure and maypresent an opportunity for early intervention. Development of a deviceto record these changes and creation of algorithms that correlate theserespiratory changes with severity of illness or injury would provide notonly a useful battlefield tool, but also one of importance in thehospital critical care setting to help evaluate and treat critically illpatients. Use in the clinic or home setting could be of use to lesscritically ill patients that nonetheless would benefit from suchmonitoring. For example, respiratory rate drops and respirations become“shallow” if a patient is overly narcotized. Respiratory rate andrespiratory effort rise with stiff lungs and poor air exchange due topulmonary edema or other reasons for loss of pulmonary compliance.However, the implications of the rate, which is the only parameterobjectively monitored is frequently not identified soon enough to besttreat the patient. A system that could provide a real time, quantitativeassessment of work of breathing and analyze the trend of respiratoryrate, intensity, localization, or variability in any or all of theseparameters is needed for early diagnosis and intervention as well astherapeutic monitoring. Such a system is needed to judge the depth ofanesthesia, or the adequacy or overdose of narcotic or other painrelieving medications.

PCA and Feedback Controls

Patient Controlled Analgesia (PCA) is a method of postoperative paincontrol that includes patient feedback. The administration of opiatescan suppress respiration, heart rate, and blood pressure, hence the needfor careful and close monitoring. The system comprises a computerizedpump that contains pain medication that can be pumped into the patient'sIV line. Generally, in addition to a constant dose of pain medication,the patient may press a button to receive care in the form of additionalmedication. However, patients are discouraged from pressing the buttonif they are getting too drowsy as this may prevent therapy for quickerrecovery. There are also safeguards in place that limit the amount ofmedication given to a patient in a given amount of time to preventoverdose. Pulse oximeters, respiratory rate and capnograph monitors maybe used to warn of respiratory depression caused by pain medication andcut off the PCA dose, but each has serious limitations regarding atleast accuracy, validity, and implementation.

Breathing Assistance Devices

Chronic obstructive pulmonary disease (“COPD”), emphysema, and otherailments have an effect of lowering the ability for the patient toprovide efficient exchange of air and provide adequate respiration. COPDis a lung disease that makes it hard to breathe. It is caused by damageto the lungs over many years, usually from smoking. COPD is often a mixof two diseases: chronic bronchitis and emphysema. In chronicbronchitis, the airways that carry air to the lungs get inflamed andmake a lot of mucus. This can narrow or block the airways, making ithard for you to breathe. In a healthy person, the tiny air sacs in thelungs are like balloons. As a person breathes in and out, the air sacsget bigger and smaller to move air through the lungs. However, withemphysema, these air sacs are damaged and lose their stretch. Less airgets in and out of the lungs, which causes shortness of breath. COPDpatients often have difficulty getting enough oxygenation and/or CO2removal and their breathing can be difficult and labored.

Cystic fibrosis (“CF”), also known as mucoviscidosis, is a geneticdisorder that affects mostly the lungs but also the pancreas, liver,kidneys and intestine. Long-term issues include difficulty breathing andcoughing up sputum as a result of frequent lung infections. Othersymptoms include sinus infections, poor growth, fatty stool, clubbing ofthe finger and toes, and infertility in males among others.

There are numerous therapies used to help alleviate the symptoms ofCOPD, CF, emphysema, and other breathing problems. For example, thepatient may wear a High-Frequency Chest Wall Oscillation (“HFCWO”) vestor oscillator. The HFCWO vest is an inflatable vest attached to amachine that vibrates it at high frequency. The vest vibrates the chestto loosen and thin mucus. Alternatively, a patient may us a continuouspositive airway pressure (“CPAP”) or bilevel positive airway pressure(“BiPAP”) device to provide mild air pressure on a continuous basis tokeep the airways continuously open in a patient who is able to breathespontaneously on his or her own. Other mechanical ventilation therapiesinclude, but are not limited to cough assist systems, oxygen therapy,suction therapy, CHFO (“Continuous High Frequency Oscillation”),ventilators, medicated aerosol delivery systems, and other non-invasiveventilation methods.

Each of these therapeutic methods has a common drawback, there is no wayof knowing how much air is actually getting into the lungs. Sometherapies use air pressure feedback to time effective oxygen therapy.This can be inaccurate and is not a direct measurement of oxygenventilation. Furthermore, therapies using masks can be inaccurate due toleakage and problems associated with mask placement. Additionally,kinking and malfunctions in the pneumatic airway circuits can provideand inaccurate measure of the amount of air which is getting into thelungs.

SUMMARY OF THE INVENTION

The present invention overcomes the problems and disadvantagesassociated with current automated physiological scoring systems byincorporating non-invasive respiratory measurements to improve diagnosisand patient stratification.

A preferred embodiment of the invention is directed to a method ofevaluating a patient. The method comprises the steps of obtainingtemporal data of the patient after an event, monitoring a plurality ofpatient parameters, compiling patient data based on the temporal dataand patient parameters, determining a state or change in state of thepatient based on the compiled patient data, and alerting medical staffof the state or change in state.

In a preferred embodiment, the temporal data is used for diagnosingand/or stratifying the patient based on the state of the patient andalerting medical staff of a diagnosis and/or stratification. Preferably,the temporal data is used to qualify or weigh the data used in makingthe determination of patient state or in making a determination of adiagnosis and/or a stratification determination based on at least one ofthe amount of time after the event or the length of time elapsedmonitoring the patient. An event is preferably at least one of asurgery, medication administration, intubation, extubation, lab tests,and a medical procedures, and patient care action. Preferably, theparameters are at least one of physiological parameters, demographics,location, clinical events, treatments, physical state, and lab testresults. In a preferred embodiment, the patient parameters are obtainedfrom sensors or inputs that measure Minute ventilation (MV) and/or % ofpredicted MV, Tidal volume (TV), Respiratory rate (RR), Ventilationflow-volume relationship, Peripheral blood oxygen saturation (SpO2),Mixed venous oxygen saturation (SvO2), End-tidal carbon dioxide (EtCO2),Sublingual and/or transcutaneous CO2, temperature, Cardiac Output (CO),Cardiac index, Perfusion, Blood pressure (BP), Heart Rate (HR),Electrocardiogram (ECG), Renal fluid volumes and rates, Pain levelscores and locations, Medication information, Patient location, Presentand prior patient diagnoses and stratification of risk based ondifferent clinical evaluation techniques, Temporal and ratiometricrelationships of parameter measurements with respect to patientlocations, Variation and rate of variation thereof, or Temporal andratiometric relationships thereof.

Preferably, the diagnosing and/or stratifying of the patient is based onat least one of supervised and unsupervised learning techniques, machinelearning, Bayesian inference networks, expert systems, principalcomponent analysis, support vector machines, time series forecasting andanalysis, fractal analysis, statistical techniques, heuristicalgorithms, deep temporal continuous and discontinuous clustering thatintegrates dimensionality reduction and temporal clustering into asingle end-to-end learning framework, maximum margin temporalclustering, fuzzy logic, and neural networks. In a preferred embodiment,the diagnosis and/or stratifications are compared to a database ofhistoric diagnosis and/or stratifications. Preferably, feedback fromcaregivers is updated in the database for future diagnosis and/orstratifications. Preferably the patient parameters include independentmeasurements of tidal volume (TV) and respiratory rate (RR) of thepatient. The TV and RR are preferably indicative of a patient's status.

Another embodiment of the invention is directed to a system ofevaluating a patient, comprising a processor adapted to obtain temporaldata of the patient after an event, a plurality of input devices incommunication with the processor adapted to monitor a plurality ofpatient parameters. Wherein the processor compiles patient dataincluding the temporal data and patient parameters, determines a stateor change in state of the patient based on the compiled patient data,and alerts medical staff of the state or change in state.

In a preferred embodiment, the temporal data is used for diagnosingand/or stratifying the patient based on the state of the patient andalerting medical staff of a diagnosis and/or stratification. Preferably,the temporal data is used to qualify or weigh the data used in makingthe determination of patient state or in making a determination of adiagnosis and/or a stratification determination based on at least one ofthe amount of time after the event or the length of time elapsedmonitoring the patient. An event is preferably at least one of asurgery, medication administration, intubation, extubation, lab tests,and a medical procedures, and patient care action. The parameters arepreferably at least one of physiological parameters, demographics,location, clinical events, treatments, physical state, and lab testresults. Preferably, the parameters are obtained from sensors or inputsthat measure Minute ventilation (MV) and/or % of predicted MV, Tidalvolume (TV), Respiratory rate (RR), Ventilation flow-volumerelationship, Peripheral blood oxygen saturation (SpO2), Mixed venousoxygen saturation (SvO2), End-tidal carbon dioxide (EtCO2), Sublingualand/or transcutaneous CO2, temperature, Cardiac Output (CO), Cardiacindex, Perfusion, Blood pressure (BP), Heart Rate (HR),Electrocardiogram (ECG), Renal fluid volumes and rates, Pain levelscores and locations, Medication information, Patient location, Presentand prior patient diagnoses and stratification of risk based ondifferent clinical evaluation techniques, Temporal and ratiometricrelationships of parameter measurements with respect to patientlocations, Variation and rate of variation thereof, or Temporal andratiometric relationships thereof.

In a preferred embodiment, the diagnosing and/or stratifying of thepatient is based on at least one of supervised and unsupervised learningtechniques, machine learning, Bayesian inference networks, expertsystems, principal component analysis, support vector machines, timeseries forecasting and analysis, fractal analysis, statisticaltechniques, heuristic algorithms, deep temporal continuous anddiscontinuous clustering that integrates dimensionality reduction andtemporal clustering into a single end-to-end learning framework, maximummargin temporal clustering, fuzzy logic, and neural networks.Preferably, the diagnosis and/or stratifications are compared to adatabase of historic diagnosis and/or stratifications. In a preferredembodiment, feedback from caregivers is updated in the database forfuture diagnosis and/or stratifications. Preferably, the patientparameters include independent measurements of tidal volume (TV) andrespiratory rate (RR) of the patient. The TV and RR are preferablyindicative of a patient's status.

Another embodiment is directed to a method for evaluating a patient. Themethod comprises the steps of: obtaining tidal volume (TV) andrespiratory rate (RR) of the patient, and diagnosing the patient basedon independent measurements of TV and RR.

Preferably, the TV and RR are obtained from the same sensor. The sensoris preferably a respiratory impedance sensor. The method preferablyfurther comprises providing an alert if the TV or RR is outside apredetermined range. Preferably, the TV and RR are independentvariables. The method is preferably used to evaluate a rapid shallowbreathing index. Preferably, the TV and RR are indicative of a patient'sstatus.

Another embodiment of the invention is directed towards a system forevaluating a patient. The system comprises a sensor adapted to obtaintidal volume (TV) and respiratory rate (RR) of the patient, and aprocessor adapted to diagnose the patient based on independentmeasurements of TV and RR.

In a preferred embodiment, TV and RR are obtained from the same sensor.Preferably, at least one sensor is a respiratory impedance sensor. Theprocessor preferably provides an alert if TV or RR is outside apredetermined range. Preferably, the TV and RR are independentvariables. In a preferred embodiment, the system is used to evaluate arapid shallow breathing index. Preferably, the TV and RR are indicativeof a patient's status.

Another embodiment of the invention is directed towards a method ofevaluating a patient. The method comprises the steps of obtainingtemporal data of the patient after an event, determining a state orchange in state of the patient based on the temporal data, and alertingmedical staff of the state or change in state.

In a preferred embodiment, the temporal data is used for diagnosingand/or stratifying the patient based on the state of the patient andalerting medical staff of a diagnosis and/or stratification. Preferably,the temporal data is used to weight the diagnosis and/or stratificationdetermination based on at least one of the amount of time after theevent or the length of time elapsed monitoring the patient. A event ispreferably at least one of a surgery, medication administration,intubation, extubation, lab tests, and a medical procedures, and patientcare action.

Preferably, the diagnosing and/or stratifying of the patient is based onat least one of supervised and unsupervised learning techniques, machinelearning, Bayesian inference networks, expert systems, principalcomponent analysis, support vector machines, time series forecasting andanalysis, fractal analysis, statistical techniques, heuristicalgorithms, deep temporal continuous and discontinuous clustering thatintegrates dimensionality reduction and temporal clustering into asingle end-to-end learning framework, maximum margin temporalclustering, fuzzy logic, and neural networks. In a preferred embodiment,the diagnosis and/or stratifications are compared to a database ofhistoric diagnosis and/or stratifications. Preferably, feedback fromcaregivers is updated in the database for future diagnosis and/orstratifications.

Another embodiment of the invention is directed to a system ofevaluating a patient. The system comprises a processor adapted to:obtain temporal data of the patient after an event, determine a state orchange of state of the patient the temporal data, and alert medicalstaff of the state or change in state.

In a preferred embodiment, the temporal data is used for diagnosingand/or stratifying the patient based on the state of the patient andalerting medical staff of a diagnosis and/or stratification. Preferably,the temporal data is used to weight the diagnosis and/or stratificationdetermination based on at least one of the amount of time after theevent or the length of time elapsed monitoring the patient. Preferably,an event is at least one of a surgery, medication administration,intubation, extubation, lab tests, and a medical procedures, and patientcare action.

The diagnosing and/or stratifying of the patient is preferably based onat least one of supervised and unsupervised learning techniques, machinelearning, Bayesian inference networks, expert systems, principalcomponent analysis, support vector machines, time series forecasting andanalysis, fractal analysis, statistical techniques, heuristicalgorithms, deep temporal continuous and discontinuous clustering thatintegrates dimensionality reduction and temporal clustering into asingle end-to-end learning framework, maximum margin temporalclustering, fuzzy logic, and neural networks. Preferably, the diagnosisand/or stratifications are compared to a database of historic diagnosisand/or stratifications. Feedback from caregivers is preferably updatedin the database for future diagnosis and/or stratifications.

Other embodiments and advantages of the invention are set forth in partin the description, which follows, and in part, may be obvious from thisdescription, or may be learned from the practice of the invention.

DESCRIPTION OF THE FIGURES

The invention is described in greater detail by way of example only andwith reference to the attached drawings, in which:

FIG. 1 is a perspective view of a four-lead embodiment of the invention.

FIG. 2 is a diagram of the Posterior Left to Right electrodeconfiguration.

FIG. 3 is a diagram of the Posterior Right Vertical electrodeconfiguration.

FIG. 4 is a diagram of the Anterior-Posterior electrode configuration.

FIG. 5 is a diagram of the Anterior Right Vertical electrodeconfiguration.

FIG. 6 is a perspective view of two four-lead configurations connectedto each other by a multiplexer.

FIG. 7 is a diagram of the ICG electrode configuration.

FIG. 8 is a perspective view of a four-lead embodiment of the inventionconnected to a spirometer.

FIG. 9 is a perspective view of a four-lead embodiment of the inventionconnected to a ventilator.

FIG. 10 is an RVM measurement (impedance) versus volume plot for slow,normal, and erratic breathing maneuvers.

FIG. 11 is a set of RVM and volume plots against time for normalbreathing.

FIG. 12 is a set of RVM and volume plots against time for slowbreathing.

FIG. 13 is a set of RVM and volume plots against time for erraticbreathing.

FIG. 14 is a plot of calibration coefficients against BMI for fourdifferent electrode configurations.

FIG. 15 is a spirometry plot that exhibits volume drift.

FIG. 16 is a volume vs. impedance plot that is affected by volume drift.

FIG. 17 is a spirometry plot that is corrected for volume drift.

FIG. 18 is a plot of volume vs. impedance, comparing data that isuncorrected and corrected for volume drift.

FIG. 19 is a flow chart that describes data analysis for the invention.

FIG. 20 is a preferred embodiment of the invention that utilizes aspeaker and a microphone.

FIG. 21 is a preferred embodiment of the invention that utilizes aspeaker and an array of microphones.

FIG. 22 is a preferred embodiment of the invention that utilizes anarray of speakers and a microphone.

FIG. 23 is a preferred embodiment of the invention that utilizes a vestfor the sensors.

FIG. 24 is a preferred embodiment of the invention that utilizes anarray built into a piece of cloth for the sensors.

FIG. 25 is a preferred embodiment of the invention that utilizes a netof sensors.

FIG. 26 is a preferred embodiment of the invention that utilizes awireless transmitter and receiver.

FIG. 27 shows graphs of impedance versus time and volume versus time forsimultaneously recorded data.

FIG. 28 illustrates an embodiment of a system of the invention.

FIG. 29 illustrates an embodiment of the device of the invention.

FIGS. 30-32 illustrate preferred embodiments of devices of theinvention.

FIGS. 33-38 depict different embodiments of lead placement.

FIG. 39 depicts an embodiment of a modified Howland circuit forcompensating for parasitic capacitances.

FIG. 40 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a HFCWO vest.

FIG. 41 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a mechanical ventilationtherapy device.

FIG. 42 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a oxygenation therapydevice.

FIG. 43 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a suction therapy device.

FIG. 44 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a cough assist device.

FIG. 45 depicts an example of a Bayesian inference system for a subsetof inputs and a subset of diagnoses.

FIG. 46 depicts an example of a schematic of expert system as applied totrue/false alarm qualification for SpO2 alarms.

DESCRIPTION OF THE INVENTION

As embodied and broadly described herein, the disclosures herein providedetailed embodiments of the invention. However, the disclosedembodiments are merely exemplary of the invention that may be embodiedin various and alternative forms. Therefore, there is no intent thatspecific structural and functional details should be limiting, butrather the intention is that they provide a basis for the claims and asa representative basis for teaching one skilled in the art to variouslyemploy the present invention.

One embodiment of the present invention is directed to a system and amethod to that uses temporal information of physiologic measurements,alarm events, patient information, and other available medicalinformation to diagnose patients with conditions and to generate scoresfor warning systems for medical staff.

Preferably, the amount of time passed since an event, such as a surgery,medication administration, intubation, extubation, lab tests, and othermedical procedures and patient care actions taken, is used as an inputto the system making decisions on patient diagnosis, deterioration, andgenerating early warning scores.

In another embodiment, the temporal information of physiologicmeasurements is used to qualify the accuracy of the measurements. Forexample, the accuracy of some medical devices, such as intravenous bloodpressure catheters, degrades over time. Preferably the system uses theduration of monitoring, the lifespan of the sensor, and the historicaleffectiveness of the sensor to qualify and weight the accuracy of themeasurement from medical devices. Furthermore, the duration of use of asensor or monitor is used to identify the alarms resulting from suchmeasurements as either “true” or “false”, or to calculate theprobability of an incorrect measurement or alarm event.

In another embodiment the system and method takes into account thelocation of the patient and the acuity of care in such location toqualify the data and alarms from physiologic monitors, predictivealgorithms and . The system and method appropriately weighs the locationof the patient (emergency room, post-anesthesia care unit, generalhospital floor, medical or surgical intensive care unit, outpatientprocedure suite, operating room, etc) in order to qualify thephysiologic measurement, the alarm and the diagnosis.

In another embodiment, respiratory metrics including minute ventilation,tidal volume, respiratory rate, flow-volume loop shape and metrics,rapid shallow breathing index (RSBI), slow deep breathing index (SDBI),and ratios and combinations of such metrics are used to generatedecisions, diagnoses and stratifications of patients for outcomesincluding but not limited to sepsis, congestive heart failure,hypovolemia, hypertension (systemic and/or pulmonary), COPD (emphysema,fibrosis), pulmonary embolism, renal failure, respiratory depression,respiratory failure, hypoxia, changes in metabolism, stratification ofrisk to develop the above conditions, stratification of acuity of carenecessary for each patient, etc.

Preferably respiratory metrics mentioned above, measured in eitherintubated or non-intubated patients, are used to calculate an earlywarning score or decide on a diagnosis for disease states including butnot limited to sepsis, congestive heart failure, hypovolemia,hypertension (systemic and/or pulmonary), COPD (emphysema, fibrosis),pulmonary embolism, renal failure, respiratory depression, respiratoryfailure, hypoxia, respiratory compromise caused by coronavirus, changesin metabolism, stratification of risk to develop the above conditions,stratification of acuity of care necessary for each patient, etc.Preferably the system uses minute ventilation and tidal volumemeasurements, as well as other available physiologic measurementsincluding heart rate, temperature, blood pressure, blood oxygenation,etc to identify early symptoms of disease states. Preferably respiratorymetrics are used in conjunction with unsupervised and supervisedlearning techniques including but not limited to machine learning,Bayesian inference networks, expert systems, principal componentanalysis, fractal analysis, statistical techniques, heuristic analysis,deep temporal continuous and discontinuous clustering that integratesdimensionality reduction and temporal clustering into a singleend-to-end learning framework, maximum margin temporal clustering, fuzzylogic, and neural networks, to calculate an early warning score ordecide on a diagnosis for sepsis.

Preferably, the ratios of respiratory metrics are used as independentinputs to a system for decision, diagnosis and stratification. Forexample the ratio of TV to RR can be dependent on the stiffness of thelungs, such that the ratio is high in cases of congestive heart failurewhere the lungs are stiff due to fluid, but the ratio is low in cases ofCOPD where the lungs are too compliant. Preferably the ratio of TV to RRis used in conjunction with supervised and unsupervised learningtechniques to diagnose either congestive heart failure or COPD.Preferably, the flow-volume relationship from which the respiratorymetrics are derived are also used to detect changes in lung stiffness byobserving changes in the flow-volume loops, either by an automatedsystem or presenting data to clinicians for confirmation.

In another embodiment, the system diagnoses opioid sensitivity by usingopioid administration events, their dosage and timing, along withrespiratory metrics, including minute ventilation, tidal volume,respiratory rate, the ratios of respiratory metrics, the variability ofrespiratory metrics, etc.

In a preferred embodiment, inputs in Table 1 are used as independentpredictors for diagnosis, decision, and stratification of patients.Preferably, respiratory metrics are used to diagnose disease statesincluding but not limited to respiratory depression or respiratoryfailure as a result of an opioid response, obstructive sleep apnea,chronic obstructive pulmonary disease, sepsis, hypovolemia, congestiveheart failure, etc. Preferably, the temporal trends in inputs to thesystem are taken in the context of previous events including but notlimited to such as a surgery, medication administration, intubation,extubation, lab tests, and other medical procedures and patient careactions taken, etc., in order to diagnose disease states.

TABLE 1 Chronic Congestive Obstructive Opioid Obstructive HeartPulmonary Inputs Response Sleep Apnea Sepsis Hypovolemia Failure Disease(COPD) Minute ↓ ↓ ↑ ↑ ↑ ↑ Ventilation (MV) Tidal Volume ↓ ↓ or ↑ ↑ or =↑ or = ↓ or = ↑ (TV) (compensated vs uncompensated) Respiratory Rate ↓or = ↓ ↑ or = ↑ or = ↑ ↓ or = (RR) Heart Rate ↓ ↑ ↑ ↑ (HR) BloodPressure ↓ ↓ ↓ ↓ = (BP) Blood ↓, late ↓, late ↓ or =, late ↓, late ↓,early ↓, late Oxygenation (SpO₂)

In another embodiment, the timing of events and alarms from a hospitalnetwork is used to calculate a-priori probabilities of events in similarpatients, which can then be used to qualify alarm events in patients inreal-time. Preferably, current alarm events are used in conjunction withhistorical alarm events to update probabilities of events in real time.

In another embodiment, the system uses information from variousphysiological parameters, demographics, clinical events, treatments, labtest results as inputs to qualify alarm events as “true” or “false” orto calculate the probability of a valid alarm, either in real-time orretrospectively. Preferably, medical staff has the option to confirm oroverride alarms event validity. In another embodiment, the real-time orretrospective feedback from medical staff is used to update the currentstatus of alarms, and/or to train via supervised or unsupervisedlearning techniques, the criteria for future alarms based on theexternal information provided by medical staff.

In a preferred embodiment, after an alarm has been sounded the systemuses the duration after the alarm has sounded but before it has beenaddressed by medical staff in order to collect data to qualify the alarmretrospectively. Preferably, the system cancels the alarm if it isdeemed to be a “false” alarm, but has not yet been addressed by medicalstaff. In another embodiment, the system updates the medical staff ofthe false alarm or the cancellation of the sounding alarm with thecriteria used for the cancellation.

In a preferred embodiment, the system uses retrospective analysis andmedical staff feedback to identify incessant false alarm events and themonitors contributing to alarm fatigue.

One embodiment of the present invention is directed to a device forassessing a patient, individual or animal that collects impedancemeasurements by placing multiple electrode leads and/or speakers andmicrophones on the body. Preferably at least one impedance measuringelement and a microphone/speaker functionally connected to aprogrammable element, programmed to provide an assessment of at leastone respiratory parameter of the subject.

Preferably, the impedance measurement is based on a plurality of remoteprobe data sets, and wherein the programmable element is furtherprogrammed to enhance at least one of the plurality of remote probe datasets; or to stabilize at least one of the plurality of remote probe datasets; or to analyze each of the plurality of remote probe data sets fordynamic range and signal to noise ratio (SNR) values. Preferably, thedevice probes are maintained in several lead configurations. In oneembodiment, variations in lead configuration allow for flexibilitydepending on the subject and test being performed. In other embodiments,variations in lead configuration allow for variability in patientanatomy. Preferably, the device maintains settings to identify validlead configurations. Preferably, the device maintains settings toidentify valid lead attachment.

Preferably, the device or method as described in a protocol embedded inthe machine instructs as to lead placement. Preferably, appropriate leadcontact is verified by the device. Preferably, the device alerts theoperator as to inadequate or inappropriate lead placement. Preferably,the device monitors continuously or intermittently and maintains alarmsto indicate when a respiratory parameter reflects a loss in ventilationor other vital function. The alarm is set based on a respiratorysufficiency index, on minute ventilation, on respiratory rate, on tidalvolume, on an inspiratory volume or flow parameter, on an expiratoryvolume or flow parameter, on variability of respiratory rate, volume,flow or other parameter generated. For example, the alarm goes off ifthe monitor detects a decrease in either respiratory frequency or depthor minute ventilation associated with hypoventilation or detects anincrease in any or all of these parameters that would suggesthyperventilation. An alarm is used on a hospital floor in comparing thepatient's current respiratory status with a baseline level based onspecific individual calibration to ventilator or spirometer. Preferably,the alarm is set based on parameters taken for the given individual froma ventilator or spirometer. More preferably the baseline level is basedon one or more of the following: demographic, physiologic and body typeparameters. An alarm is also used to alert for narcotic inducedrespiratory depression at a point that is determined to be detrimentalto the patient. Preferably, the ranges of values beyond which alarmswill be triggered are chosen by the physician or care giver for one ormore of the following: respiratory rate, tidal volume, minuteventilation, respiratory sufficiency index, shape of the respiratorycurve, entropy, fractal or other analysis parameters associated withrespiratory variability or complexity.

In another embodiment, the RVM measurements taken at any given point intime is recorded as baseline. These recorded values are correlated tosubjective impression by a physician or other health care worker ofpatient status. Subsequently, RVM is monitored and an alarm set to alerthealth care staff if a 10%, 20% or other selected percentage change inrespiratory volumes, minute ventilation curve characteristics, orvariability is noted.

The following illustrate embodiments of the invention, but should not beviewed as limiting the scope of the invention.

Impedance Plethysmograph

As embodied and broadly described herein are provided detailedembodiments of the invention. The embodiments are merely exemplary ofthe invention that may be embodied in various and alternative forms.Therefore, there is no intent that specific structural and functionaldetails should be limiting, but rather the intention is that theyprovide a basis for the claims and as a representative basis forteaching one skilled in the art to variously employ the presentinvention.

The invention preferably comprises an impedance pneumograph withintegrated electronics to convert measured impedance values to volumeand display the volume to an end-user through an electronic interface orprinted reports employing numerical or graphical representations of thedata. The impedance measuring device comprises circuitry, at least onemicroprocessor and preferably at least four leads. Preferably, where atleast two leads are used for injecting current into the subject's bodyand at least two are used for reading the voltage response of saidpatient's body.

In one embodiment, the device preferably comprises an integrated moduleto simulate a patient and allow for automated system testing anddemonstrations. Automated system tests improve the performance of thedevice and ensure that it is functioning correctly before use.

In the preferred embodiment, the device utilizes an analog divider tocompensate for slight deviations in the injected current and increasethe accuracy of acquired data. The analog divider in the preferredembodiment would be placed after the demodulator and before therectifier. In other embodiments the analog divider may be placed inother locations in the circuit including, but not limited to, after theprecision rectifier or before the demodulator.

In the preferred embodiment, the device utilizes adaptive electronicsdriven by a microprocessor to maintain the appropriate gains on thedifferent amplifiers in the circuit to prevent the signal from going outof range. The microprocessor tracks the set gains at each of thehardware amplifiers and compensates appropriately during itscalculations so that it always outputs an appropriate value.

The impedance measuring device is preferably connected to computer via adigital interface (e.g. USB, Fire wire, serial, parallel, or other kindof digital interface). The digital interface is used to prevent datafrom corruption during transfer. Communication over this interface ispreferably encrypted to further ensure data integrity as well as protectthe invention from usage of counterfeit modules (either measuring deviceor computer).

Referring now to a preferred embodiment of the invention in more detail,in FIG. 1 there is shown an impedance plethysmograph, comprising a radiofrequency impedance meter 1, a programmable element 2 contained on a PClinked to the meter, which is connected to the patient by four leads,namely a first lead 3, a second lead 4, a third lead 5, and a fourthlead 6. Each lead is preferably connected to a surface electrode, namelya first surface electrode, a second surface electrode, a third surfaceelectrode, and a fourth surface electrode.

In further detail, still referring to the embodiment of FIG. 1, theelectrodes can be made of a conductive material such as AgCl, coatedwith an adhesive, conductive material such as a hydrogel orhydrocolloid. The leads can be made of any conductive material such ascopper wire and are preferably coated with insulating material such asrubber. In a preferred embodiment, wireless electrodes are utilized toprovide current and collect and transmit data. Preferably, this leadcomposition is coupled with Bluetooth technology and a receiver.

Leads 1 and 4 are connected to a current source with a constantfrequency preferably greater than 20 KHz, which is great enough to avoidinterfering with biological signaling. The amplitude of the currentsource is preferably less than 50 mA, and below the level that wouldcause fibrillation at the chosen frequency. The differential voltagebetween leads 2 and 3 is used to calculate the impedance according toohm's law. By sampling the voltage measurements taken by the impedancemeter, the programmable element (such as a PC) tracks and plots changesin thoracic impedance that correspond to biological functions such asheartbeat and breathing. The changes in impedance are then used tomonitor pulmonary function. Preferably, the device is calibrated by amethod laid out herein to calculate the lung volumes and display them toan operator.

With reference to FIG. 28, an exemplary and preferred system includes atleast one general-purpose computing device 100, including a processingunit (CPU) 120, and a system bus 110 that couples various systemcomponents including the system memory such as read only memory (ROM)140 and random access memory (RAM) 150 to the processing 25 unit 120.Other system memory 130 may be available for use as well. The inventionpreferably operates on a computing device with more than one CPU 120 oron a group or cluster of computing devices networked together to providegreater processing capability. The system bus 110 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. A basic input/output (BIOS) stored in ROM 140 or thelike, preferably provides the basic routine that helps to transferinformation between elements within the computing device 100, such asduring start-up. The computing device 100 further preferably includesstorage devices such as a hard disk drive 160, a magnetic disk drive, anoptical disk drive, tape drive or the like. The storage device 160 isconnected to the system bus 110 by a drive interface. The drives and theassociated computer readable media provide nonvolatile storage ofcomputer readable instructions, data structures, program modules andother data for the computing device 100. The basic components are knownto those of skill in the art and appropriate variations are contemplateddepending on the type of device, such as whether the device is a small,handheld computing device, a desktop computer, a laptop computer, acomputer server, a wireless devices, web-enabled devices, or wirelessphones, etc.

In some embodiments, the system is preferably controlled by a singleCPU, however, in other embodiments, one or more components of the systemis controlled by one or more microprocessors (MP). Additionally,combinations of CPUs and MPs can be used. Preferably, the MP is anembedded microcontroller, however other devices capable of processingcommands can also be used.

Although the exemplary environment described herein employs the harddisk, it should be appreciated by those skilled in the art that othertypes of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs), read only memory (ROM), a cable or wireless signal containing abit stream and the like, may also be used in the exemplary operatingenvironment. To enable user interaction with the computing device 100,an input device 190 represents any number of input mechanisms, such as amicrophone for speech, a touch sensitive screen for gesture or graphicalinput, electrical signal sensors, keyboard, mouse, motion input, speechand so forth. The device output 170 can be one or more of a number ofoutput mechanisms known to those of skill in the art, for example,printers, monitors, projectors, speakers, and plotters. In someembodiments, the output can be via a network interface, for exampleuploading to a website, emailing, attached to or placed within otherelectronic files, and sending an SMS or MMS message. In some instances,multimodal systems enable a user to provide multiple types of input tocommunicate with the computing device 100. The communications interface180 generally governs and manages the user input and system output.There is no restriction on the invention operating on any particularhardware arrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Embodiments within the scope of the present invention may also includecomputer readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or combination thereof) to a computer, the computerproperly views the connection as a computer-readable medium. Thus, anysuch connection is properly termed a computer readable medium.Combinations of the above should also be included within the scope ofthe computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Networks may include the Internet, one or moreLocal Area Networks (“LANs”), one or more Metropolitan Area Networks(“MANs”), one or more Wide Area Networks (“WANs”), one or moreIntranets, etc. Embodiments may also be practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination thereof) through a communications network. Ina distributed computing environment, program modules may be located inboth local and remote memory storage devices.

FIG. 2 is a schematic of an embodiment of a system 200 of the invention.The electrical source originates from signal source 205. Preferably, anadjustable function generator 210 (e.g. a XR2206 chip) is used togenerate the electrical source. The function generator 210 is preferablyadjustable via a microprocessor (MP) 275 or manually. In someembodiments, the function generator can be tuned in order to improve thesignal. Tuning can occur once or multiple times. Bio-impedancespectroscopy can be used to detect levels of hydration at differentfrequencies, which can be used to calibrate function generator 210.Similarly, body fat percentages can be calculated. Signal source 205also comprises a current generator 215 (e.g. a Howland circuit). Currentgenerator 215 preferably keeps the source current constant despitechanges in pad contact (unless the contact is totally broken). In thepreferred embodiment, current generator 215 can be tuned to improveperformance, which can be done manually or automatically by the MP 275.The impedance measuring subsystem may utilize current generatingcomponents at one or more frequencies, which may be activesimultaneously, or sequentially. Voltage measuring components may befunctionally connected to one or more electrodes. The impedancemeasuring subsystem may utilize non-sinusoidal current, such as narrowcurrent pulses. The system may integrate additional sensors, such asaccelerometers, moisture and acoustics sensors, capnography or oximetrysensors.

In preferred embodiments, the pad contact quality is monitored and awarning is produced when the pad contact is broken or too poor qualityfor the electronics to compensate. Signal source 205 may also comprise acurrent monitor 220 to calculate impedance. In a preferred embodiment,signal source 205 also comprises a patient simulator 225. Patientsimulator 225 can simulate changes in the impedance with parameterssimilar to a real patient. Patient simulator 225 can be used for testingsystem 200 as well as calibration of the circuitry.

The signal from signal source 205 passes through patient 230 and isreceived by sensor 235. Preferably, sensor 230 comprises an inputamplifier 240. Input amplifier 240 suppresses the effect of poor orvariable pad contact on measurement. The gain of input amplifier 240 ispreferably controlled by the MP 275 to provide an enhanced signal to theother modules. Sensor 230 preferably also comprises a signal filter 245to remove interference from the power grid, etc. Signal filter 245 maybe a standard high-pass filter (as on FIG. 30), a demodulator (as onFIG. 31), or another signal filter. Synchronous demodulators are oftenused for detecting bio-impedance changes and stripping out motionartifacts in the signal.

In a preferred embodiment, the signal is split into two paths (as onFIG. 32). The first path demodulates the measured signal using thegenerator signal as a carrier. The second path uses a 90-degree phaserotating circuitry before demodulation. Both demodulated signals can beconverted into RMS values using voltage-to-RMS converters. Measuredseparately, the signals are summed and then the square root iscalculated. This allows for compensation for any phase shift in thesubject and for separate measurements of resistance and reactance, whichprovides valuable information for motion artifact compensation as wellas hydration levels, fat percentages, and calibration coefficientcalculations.

Additionally, sensor 230 may comprise an analog divider 250, whichdivides the measured voltage signal by the signal from the currentmonitoring circuit to calculate impedance. Sensor 230 preferably alsocomprises a precision rectifier or root mean square to direct current(RMS-to-DC) chip 255 with a low pass filter to remove the carrierfrequency. The output of sensor 230 is preferably a DC signalproportional to the patient's impedance. Sensor 230 may also comprise aband-pass filter 260 to select only the respiratory rates by filteringout the portion of the signal not corresponding to the respiration.Band-pass filter 260 may be calibrated manually or automatically by theMP 275. Preferably, sensor 230 comprises a multiplexor 265 controlled bythe MP 275 to accommodate multiple probe pairs. Preferably there are 2probe pairs, however more or fewer probe pairs are contemplated. Sensor230 may also comprise an output amplifier 270. Output amplifier 270 ispreferably controlled by the MP 275 and provides a signal to ananalog-to-digital converter (ADC) 280 for high precision digitization.Oversampling is used to reduce measurement noise which may originatefrom different sources (e.g., thermal, electronic, biological, or EMinterference). MP 275 commands ADC to take measurements with as high acadence as possible and then averages the obtained data over the timeintervals corresponding to the sampling frequency. The samplingfrequency is the frequency of the impedance sampling as it is presentedto the computer by the impedance measuring device. The frequency ispreferably set sufficiently high to monitor all the minute features ofrespiration.

Using controllable gains and oversampling preferably allows the systemto measure the impedance with extremely high effective precision(estimated 28-bit for current implementation, or 4 parts per billion).

Both signal source 205 and sensor 230 are controlled by MP 275. MP 275preferably comprises at least one ADC 280 monitoring the signalprocessing, and at least one digital output 285 to control the digitalpotentiometers, multiplexors, op-amps, signal generator, and otherdevices. Preferably, MP 275 and a computer interface (e.g., via a USBinterface, a serial interface, or a wireless interface).

Preferably, the MP computes values for respiratory rate (RR), tidalvolume (TV) and minute ventilation (MV) as well as, tracks the trends incomputed RR, TV, or MV values and performs statistical, factor, orfractal analysis on trends in real-time. The MP may tracks instantaneousand cumulative deviations from predicted adequate values for RR, TV, orMV and computes a respiratory sufficiency index (RSI).

In a preferred embodiment, the system has the capability to measure andrecord other parameters or disease states including but not limited to:temperature, blood pressure, Heart rate, SpO2, EtCO2, Arterial bloodgas, acceleration/motion, GPS location, height, weight, BMI, diagnosisof OSA, CHF, Asthma, COPD, ARDS, OIRD, Minute Ventilation, cardiacoutput, end tidal CO2, oxygen perfusion, ECG and other electrophysologicmeasurements of the heart. In a preferred embodiment, the impedancemeasuring device measures impedance cardiography and impedancepneumography simultaneously. Preferably, the additional parameters aredisplayed on-screen. Preferably, the respiratory impedance data arecombined with the additional parameters in a meaningful way to act as anadjunct to diagnosis. Preferably, the impedance data alone, or combinedwith one or more additional parameters are used to provide a diagnosisof a disease state.

In one embodiment, measurements are taken from each side of the chestindependently and used to evaluate both general pulmonary status anddifferences between right and left lung aeration or chest expansion. Anexample of this is, in the case of rib fractures, where there can bechanges attributed to damage including pulmonary contusion, decrease inmotion due to splinting or pneumothorax where both sides of the chestare monitored independently to provide side specific data. Other sourcesof localized pulmonary pathology can be evaluated including pneumonia,hydrothorax, chylothorax, hemothorax, hemo/pneumothorax, atelectasis,tumor, and radiation injury. In another embodiment, information from thedevice is used with information from an echocardiogram, radionuclidestudy or other method of imaging the heart. In a preferred embodimentthe device assists in the diagnosis of myocardial ischemia with one ofthe following: ekg, advanced electrophysiologic studies, cardiaccatheterization, echocardiogram, stress testing, radionuclide testing,CT, MRI, cardiac output monitoring by impedance measurement. In oneembodiment the device provides information that is used to help withcollection of other signals that vary with respiration such asrespiratory sounds, cardiac information, radiation detection devices,radiation therapy devices, ablation devices. In a preferred embodimentthe device can assist with the timing or data collection by anothermodality and/or using characteristics of the respiratory curve tocorrect data that is collected.

In one embodiment, the device provides information aboutbreath-to-breath variability or respiratory complexity to be used inconjunction with cardiac beat to beat variability or complexity toprovide otherwise unavailable information about cardiac, pulmonarysystems, or overall metabolic or neurologic status.

Lead Configuration

The proposed respiratory parameters evaluation technique relies on ahighly linear relation between the parameters and measured impedance. Itis not true for every electrode placement. Extensive research wasconducted to select best electrode placement which preferably satisfiesfollowing conditions:

-   -   1) Highly linear relation between respiratory volume and        measured impedance variations (i.e. correlation values above        96%).    -   2) Low level of artifacts due to patient motion.    -   3) Low variation between repetitive electrode applications.    -   4) Easy application in common clinical situation. Capability for        use with “universal calibration,” which reliably determines        scaling factors that depend on measurable patient body        parameters without preliminary calibration with        ventilator/spirometer.

Preferably, electrodes are attached horizontally to the mid-axillaryline at the level of the sixth rib. Preferably, one electrode is placedat a stable location, such as immediately below the clavicle or at thesternal notch, and another electrode is place at the bottom of theribcage or at the level of the xiphoid at the midaxillary line. However,the electrodes can be placed higher or lower on the thorax. Furthermore,electrodes may be placed in other locations and configurations (e.g.vertically along the thorax, at an angle across the thorax, or from aposition on the front of the patient to a position on the back of thepatent), depending on the subject to be tested, the test to bepreformed, and other physiological concerns (e.g. if the patient has apacemaker or other artificial device).

Preferably at least one impedance measuring element is present on one ormore electrode leads. Preferably, two or more electrodes are arranged ina linear array, grid-like pattern, or in an anatomically influencedconfiguration. Preferably, four remote probes are arranged in a lineararray. In another embodiment, multiple electrode leads are arranged as anet, vest, or array. Preferably, the one or more probes, electrode leadsor sensors are placed on the thorax or abdomen of the subject.Preferably, the device uses single use electrodes. In other embodiments,the electrodes are hydrogel, hydrocolloids, or solid gels. Preferably,the electrode utilizes AgCl, nickel, or carbon sensors. Preferably, theelectrodes come with soft cloth, foam, microporous tape, clear tapebacking or another adhesive. Preferably, different, size appropriateelectrodes exist for adults and neonates, with the adult electrodeslarger than the neonatal ones, which are preferably 1″ by ⅜″ or less(2.54 cm by 0.95 cm or less). In other embodiments, sensor electrodesare the same as the probes that deliver electrical impulses to the body,or are different from the delivery electrodes, or are wireless andtransmit data to a remote sensor. In another embodiment, the deliveryprobes are themselves sensors. In one embodiment, the stimulatingelectrode is battery powered. Preferably, the at least one respiratoryparameter is recorded for a duration of 30 seconds, continuously,intermittently, for up to at least 3, 5, 10, 20, or 50 of the subject'sbreaths, for up to at least 100 of the subject's breaths, for up to atleast 1000 of the subject's breaths, or for another duration.Preferably, the subject's impedance cardiogram is simultaneouslyrecorded.

Preferably, the at least one impedance measuring element comprises oneor more remote probes or electrode leads, or leads similar to standardEKG leads or similar to the leads used for measuring cardiac impedance,and wherein the programmable element is further programmed to analyzeone or more remote probe or electrode lead data sets collected from theone or more remote probes or electrode leads.

In one embodiment of the invention, the impedance measurement subsystemreads impedance from multiple channels. In a preferred embodiment, asecondary voltage sensing channel is arranged at an angle to a primaryvoltage sensing channel. In one embodiment, the two channels sharecurrent generating electrodes. In one embodiment, the two channels alsoshare one of the voltage sensing electrodes. Data from the two or morechannels may be used in an adaptive algorithm to determine and suppressnoise from motion.

Lead configuration is critical for the performance of the device in anyembodiment. Preferably, one or more leads are placed on the thorax. Inone embodiment, leads are placed on the thorax and abdomen to measurebreathing from different regions of the body such as the thorax or theabdomen. Differences in the location of body motion associated withbreathing produces information that is useful clinically for diagnosisof physiologic state and monitoring of disease and can be compensatedfor in calculations. Leads are placed on the thorax, neck and head inalternate configurations. In one embodiment, leads are placed indifferent configurations based on anatomic locations and spaced eitheraccording to specific measured distances or anatomic landmarks or acombination of both. In one embodiment, modifications of the spacingrelative to body size are implemented. Preferably these modificationsare related to anatomic landmarks. In a preferred embodiment, thespacings remain relatively the same for patients of all sizes fromneonates to obese patients, ranging from 250 g to 400 kg. In anotherembodiment, the spacings vary based on an algorithm reflecting body sizeand habitus. Other configurations have the advantage of determiningdifferential motion of one hemithorax vs. the other which is useful indiagnosing or monitoring unilateral or asymmetric pathology such aspneumothorax, hemothorax, empyema, cancer.

Referring now to FIG. 2, there is shown one embodiment with a specificelectrode configuration called Posterior Left to Right (PLR), in whichthe first electrode 7 is placed 6 inches to the left of the spine at thelevel of the xiphoid process, the second electrode 8 is placed 2 inchesto the left of the spine at the level of the xiphoid process, the thirdelectrode 9 is placed 2 inches to the right of the spine at the level ofthe xiphoid process, and the fourth electrode 10 is placed six inches tothe right of the spine level with the xiphoid process. The advantage ofplacing the electrodes in this configuration is that both lungs arefactored into the reading and high level of signal.

Referring to FIG. 3, there is shown the second specific electrodeconfiguration called Posterior Vertical Right (PVR), in which the firstelectrode 11 is placed midway between the midaxillary line and the spinejust beneath the scapula, the second electrode 12 is placed two inchesbeneath electrode 1, the third 13 electrode is placed two inches beneathelectrode 2, and the fourth electrode 14 is placed beneath electrode 3.The advantages of this configuration are the reduction of electrodemovement due to thoracic expansion and less cardiac interference. Thisposition has the benefit of little to no volume change betweenelectrodes and less heart noise.

Referring to FIG. 4, there is shown the third specific electrodeconfiguration called Anterior to Posterior (AP), in which the firstelectrode 15 is placed 6 inches to the right of the right midaxillaryline at the level of the xiphoid process, the second electrode 16 isplaced 2 inches to the right of the right midaxillary line at the levelof the xiphoid process, the third electrode 17 is placed 2 inches to theleft of the right midaxillary line at the level of the xiphoid process,and the fourth electrode 18 is placed 2 inches to the left of the rightmidaxillary line at the level of the xiphoid process. This positioncaptures the most volume change, which is useful for determination oflocalization of breathing.

Referring to FIG. 5, there is shown the fourth specific electrodeplacement called Anterior Vertical Right (AVR), in which the firstelectrode 19 is placed immediately beneath the clavicle midway betweenthe xiphoid and midaxillary line, the third electrode 20 is placed atthe level of the xiphoid in line with the first electrode, the secondelectrode 21 is placed 4 inches above the third electrode, and thefourth electrode 22 is placed 4 inches below the third electrode. Thisposition is useful for neonates and other patients whose characteristicsprevent the operator from placing leads on the posterior. Otherfour-probe positions are placed vertically and horizontally on theabdomen and thorax, equidistant from each other or at specificallymeasured distances. Probe positions are also placed at physiologicallandmarks such as the iliac crest or third intercostal space. Probeplacement on both the abdomen and thorax allows the relationship betweenchest and abdominal breathing to be determined. This relationshipassists in diagnosis and monitoring of therapeutics.

In addition to the aforementioned four-probe configurations, theseconfigurations can be modified to include more probes by adding probesequidistant between the positions, for example, by adding electrodes inbetween electrodes 1 and 2, 2 and 3, 3 and 4 in the AP configuration twoinches from each electrode in line with the placement. With a largenumber of electrodes, they can be placed in a grid pattern equidistantfrom each other; this configuration will be further discussed below.Other placements for 2 or more leads include around the thorax atequidistant points at a constant height such as the xiphoid process. Thespecific placement for the 24 lead system is within a linear array with12 leads equally spaced in a linear on the chest and back respectively.Such a grid or array can be implemented within a net or vest to be wornby the patient. In one embodiment, the device provides a tabledescribing lead placement alternatives and provides a measurement deviceto assist in probe placement. In one embodiment, measured distancesbetween leads are confirmed automatically by the leads which havepositioning sensors and/or sensors which can determine distance from onesensor to another sensor or sensors.

Referring now to FIG. 6, there is shown several electrode configurations23, connected together by means of an analog multiplexer 24 andconnected to a radio frequency impedance meter 25 and a programmableelement 26 such as a PC. There is shown an embodiment of the deviceimplementing the lead and multiplexor configurations shown in theprevious figures, FIGS. 2 and 3. In FIG. 6, each lead is connected toseveral different electrodes by means of a multiplexer. The advantage ofthis configuration is that it allows the device to digitally switch theelectronic inputs and outputs of the DAS and effectively switch theelectrode configuration in order to gather data on impedance in severaldirections nearly simultaneously. For example, a 12-electrode system iscomprised of four different sets of leads, with the first set going tothe corresponding first electrode in each configuration, the second setof leads going to the corresponding second electrode in eachconfiguration, and so forth.

Electrode configurations are also made to correspond with anatomicpositions on the thorax, abdomen, and limbs, such as a resting ICGposition shown in FIG. 7 where the first electrode 27 is place on theforehead, the second 28 above the left clavicle, the third 29 on themidaxillary line level with the xiphoid, and the fourth 30 on themidaxillary line immediately above the iliac crest.

Each electrode configuration will be affected by motion in differentways. For instance, movement of the right arm will cause a motionartifact on any lead placement which traces impedance across the rightpectoral, latissimus, trapezius muscles, and other muscles of the chestand upper back. By noting differences between the shapes, derivatives ormagnitudes of simultaneously recorded signals from different leadplacements, local motion artifacts can be identified and subtracted fromthe impedance signal.

In one embodiment, the probes are manufactured in a linear strip with adelivery and sensor pair at each end and having a fixed distance betweenthe delivery and sensor electrode to form a discrete pad. In a preferredembodiment, there is a compliant strip in-between the two pads that canbe stretched to permit appropriate patient specific positioning based onanatomic landmarks. Preferably the material, once stretched, willmaintain its extended configuration.

Probes

Referring now to FIG. 23, there is shown an embodiment of the device inwhich the one or more remote probes, which are embodied as surfaceelectrodes, speakers and/or microphones, are integrated into a vest 46connected to an impedance plethysmograph 47 using a cable. The advantageof this embodiment is that the position of leads is determined by themanufacturer of the vest, and thus they are standardized. That is, theuse of the vest eliminates operator error with respect to leadconfiguration. In an alternate embodiment, the probes and actuators arewireless. In an alternate embodiment, the vest also includes leads thatcover the abdomen.

Referring now to FIG. 24, there is shown an embodiment of the device inwhich the one or more remote probes are integrated into an array 48where the electrodes are connected by a compliant piece of cloth ornetting which is be pressed gently onto the patient's skin. The benefitof this configuration is that the inter-electrode distance isstandardized by the array manufacturer, thus lessening operatordependent error with respect to electrode configuration.

Referring now to FIG. 25, there is shown an embodiment of the device inwhich the one or more remote probes are connected to each other bystrings, forming a net 49 which can be applied to the patient's skinquickly and effectively. The benefit of said embodiment is that theinter-electrode distance as well as the relative positions of electrodesto one another are standardized, thus lessening the effects of operatordependent error. In another embodiment, elastic stretch of the stringsprovides probe adjustment for different body habitus. Preferably, thestretch material would provide a measurement of the distance either tobe read on the material or by relaying information relative to stretchto the device. Preferably, the strings would have attached displacementsensors such as linear displacement transducers or strain gaugesfunctionally connected to the programmable element to relay informationabout the length each string of the net is stretched. Preferably, theprogrammable element is further programmed to account for changes inlead placement relayed to it from the displacement sensors.

Referring now to FIG. 26, there is shown an embodiment of the device inwhich the one or more remote probes are functionally connected to aremote transmitter 50, and in which the programmable element 51 isconnected to a remote receiver. The communication protocols proposed forthe system range from a limited scope to a vastly networked system ofseveral nodes. This provides a foundation for an unlimited number of usecases. In one embodiment of the remote communication protocol a closerange high frequency system such as Bluetooth v4.0 is used. Thisemulates a wireless solution of what a RS-232 wired connection wouldprovide. This enables the communication of two devices in close rangequickly and securely. In another embodiment a roughly 802.11 compliantprotocol is used to generate a mesh network comprised of the nearestdevices. This mesh network incorporates all of the devices in a givenunit. The unit size is without bound since the addition of individualnodes increases the range (range and unit size are directly proportionalsince the network is comprised and governed by the nodes themselves—nounderlying infrastructure is required). Only a vast outlier is left outof this network. This means that in order for the outlier to be omittedthe nearest currently connected node must be unequivocally out of rangefor the outlier to communicate with. These services, specifically thehardware, are capable of running/polling without the usage of a main CPU(minimizes battery usage). This is useful because when a device is notbeing read it can just act as a relay node. The nature of the systemminimizes power requirements (increasing longevity of service), supportsasymmetric links/paths, and enables each node to play multiple roles inorder to benefit the network.

Another embodiment requires connection to a LAN or WAN network, theremote procedure is catalyzed by a user-driven event (button press,etc). This generates a unique identifier, for a digital receipt of thedata transaction, on each phone coupled with device specificinformation. This information is supplemented with a GPS location todistinguish the devices locations. Since the data transmission wasinitiated by both parties at a precise time, coupled with GPSinformation, the system is capable of securely identifying both partiesby location, UID, and device identifier. All methods are secured withanonymity heuristics and encryption. This will prevent snooping of data,a problem presented by a “man-in-the-middle” attack.

Another embodiment of the device utilizes one or more electrical probesimplanted in the body. In one embodiment of the invention, the implantedprobes are connected to a cardiac pacemaker. In another embodiment, theimplanted probes are connected to an internal automated defibrillator.In another embodiment, the implanted probes are connected to a phrenicnerve stimulator. In another embodiment the implanted probes areconnected to a delivery pump for pain medication, local anesthesia,baclofen, or other medication. In another embodiment, the implantedprobes are connected to another implanted electronic device. Preferablythe connections are wireless. Referring now to FIG. 33, electrodeconfiguration XidMar is show.

Configuration XidMar is a two channel configuration with electrode 1 onthe xiphoid process and electrode 4 on the right midaxillary line,horizontally aligned with electrode 1. Electrode 2 a is 1 inch to theleft of electrode 1, while electrode 3 a is 1 inch to the right ofelectrode 4. Electrodes 2 a and 3 a are used to record the voltagesignal on channel a. Channel b is recorded using electrodes 2 b and 3 bwhich are found 1 inch below the corresponding channel a electrodes.

FIG. 34 shows the StnMar electrode configuration in which electrode 1 islocated just below the sternal notch and electrode 4 is located on theright midaxillary line, horizontally aligned with the xiphoid process.Electrode 2 a is located 1 inch below electrode 1, and electrode 3 a islocated 1 inch to the right of electrode 4. Channel b is at an angleapproximately 45 degrees to channel a. Electrode 2 b is located on thexiphoid process and electrode 3 b is located 1 inch below electrode 3 a.

FIG. 35 shows the StnlMar electrode location in which electrode 1 islocated just below the sternal notch and electrode 4 is located on theinferior right midaxillary line at the bottom of the rib cage. Electrode2 a is located 1 inch below electrode 1, and 3 a is located 1 inch tothe right of 4. Electrode 2 b is located on the xiphoid process andelectrode 3 b is located 1 inch below electrode 3 a.

FIG. 36 shows the McrMar electrode configuration in which electrode 1 islocated on the right midclavicular line just below the clavicle andelectrode 4 is located on the right midaxillary line horizontallyaligned with the xiphoid process. Electrode 2 a is located 1 inch belowelectrode 1 and electrode 3 a is located 1 inch to the right ofelectrode 4. Electrode 2 b is located on the xiphoid process, andelectrode 3 b is located 1 inch below electrode 3 a.

FIG. 37 shows the McrlMar electrode configuration in which electrode 1is located on the right midclavicular line just below the clavicle andelectrode 4 is located on the inferior midaxillary line approximately atthe bottom of the ribcage. Electrode 2 a is located 1 inch belowelectrode 1 and electrode 3 a is located 1 inch to the right ofelectrode 4. Electrode 2 b is located on the xiphoid process andelectrode 3 b is located 1 inch below electrode 3 a.

FIG. 38 shows the MclMar electrode configuration in which electrode 1 islocated on the left mixclavicular line just below the clavicle andelectrode 4 is located on the right midaxillary line, horizontallyaligned with the xiphoid process. Electrode 2 a is located 1 inch belowelectrode 1 and electrode 3 a is located 1 inch to the right ofelectrode 4. Electrode 2 b is located on the xiphoid process andelectrode 3 b is located 1 inch below electrode 3 a.

The electrode configurations shown in FIGS. 34-38 can utilize eitherchannel a, channel b, or both simultaneously to measure data.

In one embodiment of the invention, the system is adapted to perform animpedance tomography scan utilizing a one or more pairs of sourceelectrodes and one or more voltage sensing electrodes. The scan iscompleted by taking a series of measurements with a movable electrodewhich is applied to the skin. The movable electrode forms a voltagemeasuring pair for impedance reading with at least one other electrode.The movable electrode may be coated in hydrogel which may be appliedmultiple times. In another embodiment of the invention, the electrodecontains a hydrogel dispenser for each application. In this embodiment,hydrogel is stored in an internal pouch or syringe and there aredevices, such as a mechanical button or squeeze tube, which allows theuser to dispense hydrogel onto the electrode. In one embodiment of thedevice of the invention, the system directs the user to sweep themovable electrode between predetermined points on the body as indicatedon the user interface or on a reference card. In another embodiment, theuser may place the movable electrode from point to point and the systemsenses the location of the electrode using a camera, sonar, radar orother device.

The secure adhesion of electrodes is determines the quality of impedancereadings. In one embodiment of the invention, the system detects thequality of adhesion and reports an index of adhesion to the user. Inanother embodiment, the system reports problems with adhesion if theindex crosses a specific threshold. In a preferred embodiment of theinvention, there are multiple voltage sensing channels arranged in astraight line. This can be accomplished using five electrodes arrangedin a line. Referring to the five electrodes by letter, electrodes A andB are placed close together on one end of the line, electrodes D and Eare placed close together on the other end of the line. Pair A-B andpair D-E may be placed 3-24″ apart from each other. Electrode C isplaced somewhere between the two pairs. Impedance is measured on threechannels, B-C, C-D and B-D. If all the electrodes are adhered well, thesum of Z_(BC) and Z_(CD) should be close to Z_(BD). The differencebetween the measures, or the ratio of the difference to the fullmeasurement can be used to determine the index of adhesion quality.

In one embodiment of the invention, Electrode C is not placed in astraight line with the other pairs of electrodes. In this case,impedance is measured on channels B-C and B-D. The ratio between theimpedance on the two channels Z_(BC) and Z_(BD) is used to determine theindex of adhesion quality. In another embodiment of the invention, thecurrent driven through electrodes A and E is measured. The currentmeasurement or variability in the current measurement can be used todetermine the index of adhesion for electrodes A and E.

Electrical connectors have inherent capacitance which can affectimpedance measurements. In one embodiment of the invention, the systemcompensates for the capacitance of cables, leads or other electricalconnection between the impedance measuring subsystem and thepatient-connected electrodes. In one embodiment, this is accomplished byan inductor within the impedance measuring subsystem. In anotherembodiment, a compensating inductor is integrated into a patient cableor leads which connect the impedance measuring subsystem to thepatient-connected electrode pads. In another embodiment a compensatinginductor is embedded into an integrated electrode PadSet. In anotherembodiment, a modification of a Howland circuit which consists ofcapacitors C₁ and C₂ with values chosen to compensate for parasiticcapacitances C_(c) is used (see FIG. 39).

To achieve high clinical relevancy and good definition of respiratorycurves, the impedance measurement subsystem should to be able todetermine small variations in patient impedances on top of a relativelyhigh baseline background with a high resolution. Therefore, there arestringent requirements on the absolute and relative impedancemeasurement errors. To obtain sufficient precision one or more of thefollowing design solutions can be used: (1) the electronic design can bebased on high precision/low temperature drift electronic components; (2)a high precision analog divider can be used to obtain the ratio betweenmeasured voltage and monitored source current, compensating forvariations in the source current; (3) the same voltage can be used forsource current generation and as an ADC reference, compensating forvariations in the reference voltage; (4) external calibrated impedancestandards can be used to calibrate and verify the impedance measurementsubsystem performance. The calibrated system is preferably connected tothe impedance standard with the same trunk cables used for patientmeasurements, providing verification of overall system performance. (5)The impedance measuring subsystem can have a built-in calibratedimpedance standard, allowing on-site verification and recalibration. Inone embodiment built-in standard is attached to the system via anexternal service port. The calibration is conducted by connecting the“patient” end of trunk cable back to the service port on the device andrunning calibration procedure available through the device's GUI. (6)The calibration can be compleated by varying impedance of the built-instandard over the whole range of the measured patient impedances toderive a device model, which can be used during patient measurements toachieve high-precision results. (6) The temperature model of the devicecan be derived by placing the device into a thermostat and measuringdrift in the measured value as a function of internal devicetemperature. The internal device temperature can be monitored via abuilt-in thermal sensor. During patient measurement, a measurementcorrection is calculated using the thermal sensor's reading and appliedto the measured values.

Active Acoustic System

For acoustic measurement of lung volumes, preferably the devicecomprises at least one speaker and at least one microphone. Preferablythe at least one speaker and microphone are arranged as a net, vest, orarray. Preferably the at least one speaker switches between discretefrequencies or broadcasts broad spectrum noise. Preferably, numerousspeakers are active simultaneously, broadcasting different acousticsignals. Preferably, numerous microphones are active simultaneously andrecord the measured acoustic properties of the thorax which can becorrelated to lung volume as well pathologies of the lungs. Preferably,the microphones also record sounds that originate in the lungs such aswheezing, squawks, and crackles, which can be indicators of numerouschronic and acute pulmonary diseases. Preferably the lung sounds arerecorded and identified as they are modified by the active signal.Preferably an algorithm analyzes the number and position of wheezes,squawks, and crackles to predict asthma and other pulmonary diseases. Inone embodiment, acoustic data are combined with impedance data to helptime the acoustic measurements relative to the respiratory cycle. In oneembodiment acoustic data are combined with impedance data for thepurposes of diagnosis or monitoring of disease. An example of this iscongestive heart failure where stiffness creates characteristic changesin impedance curves and there are also changes in lung sounds associatedwith congestive heart failure. Combination of the data providesadditional information.

Referring now to FIG. 20, there is shown a device in which a speaker 38is attached to the chest of a patient, and insulated with sounddampening foam 39. A microphone 40 is attached to the patient's back andis insulated with sound dampening foam. Both the speaker and themicrophone are functionally connected to a programmable element 41, forexample a computer with installed analysis software such as MATLAB. Theoutput element provides data relating to the patient's respiration tothe operator in real time. The speaker generates an acoustic signalwhich is recorded by the microphone. Signal generation and recording aretimed and synchronized by the programmable element. Analysis softwareuses features of the recorded sound wave to evaluate the acousticproperties of the thorax, which can be used to estimate lung volume.Said signal features include but are not limited to: frequency-dependentphase shift, and amplitude attenuation. Preferably, the speaker switchesbetween discrete frequencies of sound or generates broad spectrum whitenoise.

In another embodiment of the device, the microphone is also used todetect sounds which originate within the lungs such as crackles, squawksand wheezes. In one embodiment, the programmable element of the devicewill employ software algorithms to detect associate acoustic patternsand inform physicians. In one embodiment, the acoustic system willinterface with an impedance based system as well.

Referring now to FIG. 21, there is shown an embodiment of the device inwhich an array of microphones 42 is used to record transmitted soundfrom different regions of the thorax. Preferably microphones recordsimultaneously. Preferably, the programmable element 43 selects themicrophone with the best signal to noise ratio for analysis. Preferably,the programmable element combines the data from different channels inorder to maximize the accuracy of lung volume estimates and localizepathologies of the lungs including tumor formation, bleeding, and tissuedegradation.

Referring now to FIG. 22, there is shown an embodiment of the device inwhich an array of speakers 44 is used to generate acoustic waves.Preferably the programmable element 45 controls each of the speakersindividually, and switches between speakers to allow the device tomeasure acoustic properties of the thorax in many different directions.Preferably, the programmable element will activate each speakersimultaneously with signals of unique frequencies so that the signalfrom each speaker can be separated in the recorded signals. Preferably,the programmable element combines the data from different channels inorder to maximize the accuracy of lung volume estimates and localizepathologies of the lungs including tumor formation, bleeding, and tissuedegradation.

Patient Data Entry

Preferably, the device software maintains a user-friendly GUI (GraphicalUser Interface). Preferably, the GUI contains a color coding system toaid operators in quickly making diagnoses and decisions for patientcare. In one embodiment, the GUI presents a numerical RVM measurement.In one embodiment the GUI presents a respiratory sufficiency index(RSI). In one embodiment, the GUI presents a respiratory waveform.

In the software present in all embodiments of the device, patient datais preferably recorded by the user prior to testing. The user isprompted to enter patient data. The data recorded includes any or all ofthe following: patient height, weight, chest circumference duringmaximum inspiration, chest circumference during normal end-expiration,age, gender, ethnicity, and smoking history. In one embodiment, posturewhen testing is also input into the device within the programmable GUI.Variations in posture may lead to different breathing patterns and tidalvolumes. The device accepts posture inputs such as supine and seated andstanding. The ability to test patients in multiple postures is helpfulwith noncompliant patients such as neonates or obtunded patients.

In one embodiment, the device calculates BMI. In a preferred embodiment,an algorithm in the device or on a look up table calculates a“calibration coefficient” that corrects for patient size and bodyhabitus to provide a universal calibration to deliver an absolutemeasurement. The calibration coefficient may be obtained by combiningpatient information with the data recorded off the probes applied.Preferably, the physical location of the probes is also entered. Duringthe data acquisition, the calibration algorithm may validate the dataand their consistency with the patient information entered, and maysuggest combination of the input parameters that is most consistent withthe data recorded, as well as a suggestion for the operator to re-checkthe patient's information. As data is being acquired, the calibrationalgorithm may suggest and/or perform re-adjustment based on signalpattern recorded off probes, and/or provided by an operator as normal orabnormal. In another embodiment, the device calculates BSA or anotherindex of body shape or size. In one embodiment, the system displayspredictive values for patient results based on the aforementionedpatient data. In one embodiment, the device also provides a percentagecomparison against these values within displayed results to furtherinform the clinician of patient parameters or condition based onstandard tables of spirometric data created by Knudsen, Crapo, orothers. In one embodiment, the patient's demographics and/or bodymeasurements are entered and the device suggests the lead configurationand/or the spacing of the leads and/or the size or characteristics ofthe lead for that patient.

In one embodiment, the device assesses signal variation and adjustsdisplay parameters, calibration parameters and or intermediatecalculations in response to the variation. In one embodiment the deviceassesses variation in one or more features of the signal includingbaseline, mean, minimum, maximum, dynamic range, amplitude, rate, depth,or second or third order derivatives of any items in the list.

In one embodiment, the device calculates a calibration coefficient toconvert a raw or processed impedance trace to a respiratory volumetrace. In one embodiment, the calibration coefficient is calculated froma range of physiological and demographic parameters. In one embodiment,the device of the invention automatically adjusts the calibrationcoefficient in response to variation in the parameters. In oneembodiment, the device automatically adjusts the calibration coefficientin response to one or more of: respiratory rate, baseline impedance, ormean impedance.

In one embodiment, the device includes one or more of, respiratory rate,baseline impedance, or mean impedance in the calculation of thecoefficient, or a correction factor for the calibration coefficient. Inembodiments in which the calibration coefficient is based on atime-variable parameter, such as respiratory rate, baseline impedance ormean impedance, the device automatically adjusts the calibrationcoefficient to account for the variation in the parameter.

In one embodiment, the device adjusts the calibration coefficient basedon the assessment of variation in the signal. In one embodiment wherethe calibration coefficient is used to convert a raw impedance signal toa respiratory volume trace, the calibration coefficient is basedpartially on respiratory rate.

In one embodiment, the device adjusts the display of a dataset inresponse to variation in the dataset. The dataset is made up of a rawsignal from a sensor, a processed signal from a sensor, or thecalculated metrics or parameters.

In one embodiment, the device adjusts the minimum of the y-axis on adisplayed chart in response to variation in the dataset. In oneembodiment, the minimum of the y-axis on a displayed chart is equal tothe minimum of the dataset. In one embodiment the minimum of the y-axison the displayed chart is equal to the minimum of the dataset within aspecific window. In one embodiment, the window over which the relevantminimum of the dataset is calculated is the same as the window overwhich the data is displayed. In one embodiment, the minimum of they-axis on the displayed chart is equal to the minimum of the datasetwithin the display window minus a coefficient or percentage of theminimum value.

In one embodiment, the device adjusts the range of the y-axis of thedisplayed dataset is to account for variation in the dataset. In oneembodiment the range of the y-axis of a displayed dataset is equal tothe dynamic range of the dataset. In one embodiment the range of they-axis of the displayed dataset is equal to the dynamic range of thedataset within a specific window. In one embodiment, the y-axis of thedisplayed dataset is equal to the dynamic range of the dataset within aspecific window, plus a constant, or a percentage of the dynamic range.

In one embodiment, the device adjusts the range of the y-axis of adisplayed dataset based on statistics of a feature of the dataset. Inone embodiment, the device sets the range of the y-axis to be equal tothe mean amplitude of the signal plus the standard deviation of theamplitude of the signal within a specified window multiplied by acoefficient. In one embodiment, the device adjusts the range of they-axis of a displayed dataset to be equal to the mean amplitude of thesignal plus the variance of the amplitude of the signal within aspecified window multiplied by a coefficient. In one embodiment, thedevice calculates the amplitude of respirations in the dataset. Thedevice then removes outliers at the high end, low end or which havefeatures which appear unrelated to the intended measured parameter. Thedevice then adjusts the range of the y-axis to be equal to the mean ofthe amplitude of the dataset plus the standard deviation of the datasetmultiplied by a coefficient.

In one embodiment, the device automatically adjusts the midpoint of they-axis of a chart of a dataset in response to variation in the dataset.In one embodiment, the device sets the y-axis to be equal to the mean ofthe dataset within a specific window. In another embodiment, the devicesets the y-axis to the equal to the median of the dataset within aspecific window. In one embodiment, the device sets the midpoint of they-axis to the result of a function of the statistics of the dataset.

Calibration Method

The calibration coefficient is calculated in a novel way. In thepreferred embodiment, the device contains circuitry and software thatautomatically calibrates the device. In one embodiment, calibration isaided by data acquired through bioelectrical impedance analysis, aprocess which measures tissue impedance on one or more channels atvarious frequencies. In this embodiment, data from bioelectricalimpedance analysis may be used to calculate certain characteristics ofthe subject including, but not limited to, hydration level, baselineimpedance and body composition. A low level of hydration causes theelectrical impedance of the body to be greater. A high level of fat inthe body would also cause an increase in the average electricalimpedance of the body, but likely a decrease in overall impedance aselectricity passes through the path of least resistance. Muscle is muchmore vascular than fat and contains more conductive electrolytes, so amuscular patient's body would have much lower electrical impedance thana similarly size person who was not very muscular. Scaling thecalibration factor based on these inputs makes it more accurate.

Calibration of the device of the invention preferably comprisespredictions for respiratory rate, tidal volume and minute ventilationbased on the metabolic requirements of body tissue. Predictionspreferably involve multiplying the patient's measured body weight, orideal body weight by a volume of air, or volume of air per minuterequired by a unit of body weight. The ideal body weight is determinedfrom a patient's height, race, and/or age and may further be determinedwith one or more of the Devine, Robinson, Hamwi, and Miller formulas.

In one embodiment, the calibration coefficient is calculated from apatient's demographic information, including but not limited to: sex,age, and race. In another embodiment, the calibration coefficient iscalculated from a patient's physiological measurements including but notlimited to body type, height, weight, chest circumference measured atdifferent points of the respiratory cycle, body fat percent, bodysurface area, and body mass index. In another embodiment the calibrationcoefficient is calculated based on the measured value of the ECG signalrecorded at different points. In more detail, the ECG is recorded byelectrodes at various locations on the thorax and abdomen. In oneembodiment, the differential voltage recordings at different electrodesare used to calculate the average baseline impedance and estimate theresistivity of the patient's thorax in various directions. In anotherembodiment the calibration coefficient is calculated based on thepatient's baseline impedance to an external current source as measuredbetween electrodes in a bipolar configuration, tetrapolar configurationor other configuration comprising 2 or more leads. The locations ofthese electrodes are placed in a range of configurations over the wholebody. In another embodiment, demographic characteristics are combinedwith baseline impedance measurements for calibration. In anotherembodiment anatomic information is combined with baseline impedancemeasurements for calibration. In a preferred embodiment, known volumesrecorded on a spirometer or ventilator are combined with demographicinformation and baseline impedance. In such embodiments, the system maysimultaneously measure impedance and volume (using a spirometer,ventilator, or other similar device). The system then computes aspecific transformation between impedance and volume as an input to theconversion algorithm

In another embodiment, a dynamic calibration based on additionalparameters obtained using the impedance measuring subsystem andconsisting of overall patient impedance (including skin and fat layerimpedances), internal organs impedance (baseline impedance) and itsvariations, and the shape of the respiratory curve is implemented.

Ongoing or intermittent checks of calibration are preferably undertaken.In a preferred embodiment of the device, calibration is recalculatedwith the recording of each sample. In another embodiment, the device isregularly recalibrated based on a timer function. In another embodiment,the device is recalibrated whenever the baseline impedance varies fromthe baseline by a certain threshold such as 10%. In another embodiment,the device is recalibrated whenever tidal volume or minute volume variesfrom baseline levels or predicted levels by a certain threshold, such as20%, where predicted values are calculated using the formulas publishedby Krappo, Knudson, and others.

Ongoing or intermittent checks of calibration may be undertaken.Preferably this involves an internal check to internal phantom.

Preferably ongoing or intermittent checks of baseline impedance are beused to recalibrate or reaffirm calibration. Preferably ongoing orintermittent readings from each hemithorax individually or incombination are used to recalibrate or provide data for recalibration.

Preferably, recalibration is performed automatically or by alerting acaregiver of required modification or requiring additional steps to betaken by the caregiver, such as recalibrating with a ventilator orspirometer.

In one embodiment calibration is done through measurement electrodepairs. In another embodiment, calibration is done through additionalelectrodes. In another embodiment, calibration is done all or in part byrepurposing measurement electrodes and using the sensor as the deliveryelectrodes and the delivery electrodes as the sensor electrodes.

Preferably the calibration electrodes are placed in specific locationsand/or at specific distances apart on the abdomen and thorax. In anotherembodiment, one or more of the leads are placed a specified distanceapart on the forehead. In another embodiment of the device, themagnitude of the ICG signal across an acceptable electrode configurationwith or without an estimation of the heart volume is used to determinethe baseline impedance and calibrate the RVM data to respiratory volume.Preferably the calibration coefficient is calculated using a combinationof the 5 previously mentioned methods.

Universal Calibration

While relations between respiratory and impedance variations are highlylinear, the “scaling factor” between those values vary significantlyfrom one patient to another. There is also day-to-day variation for thesame patient. The day-to-day variations are correlated to some extentwith physiological parameters measured by the RMV device and can besignificantly compensated for. The residual day-to-day variations forthe same patient are smaller than typical measurement error. In apreferred embodiment, this residual variation can be managed withexisting ancillary measurements. In a preferred embodiment, thisresidual variation can be managed using ongoing or intermittentrecalibration by any of the methods previously described.

In one embodiment, the “scaling factor” varies between patients by aboutan order of magnitude. In a preferred embodiment, this factor can bedetermined precisely by preliminary calibration with a spirometer orventilator data or other data set. In a preferred embodiment, the RMVdevice is used for measurement of respiratory parameters withoutpreliminary calibration. Preferably, a reliable procedure of deducingthis factor from measurable patient physiological parameters is used forcalibration. Such procedure allows the determination of the “scalingparameter” with sufficient precision to satisfy measurement requirementsfor all proposed device applications.

In one embodiment, measurements of respiratory motion derived from atechnology including impedance plethysmography, accelerometers placed onthe body, video images, acoustic signals or other means of trackingmotion of the thorax, abdomen or other body parts is calibrated orcorrelated with another technology that assesses respiratory status. Ina preferred embodiment, respiratory motion detection derived fromimpedance measurements is calibrated with spirometry. In one embodimentrespiratory motion detection is calibrated or correlated with end tidalCO2 measurements. In one embodiment, respiratory motion detection iscalibrated or correlated with ventilator measurements of flow and/orvolume. In one embodiment, respiratory motion is calibrated with afull-body plethysmograph. In one embodiment, baseline RVM measurementsof a given patient are taken in conjunction with standard spirometrymeasurements and a calibration coefficient for that particular patientis derived. Later in the postoperative period or otherwise, thecalibration coefficients are used to obtain quantitative lung volumemeasurements for that patient. In a preferred embodiment, suchcalibration coefficients are combined with current baseline impedance orother physiologic measurements for ongoing or intermittent calibration.In one embodiment, preoperative measurements are used to derive acalibration coefficient which is then used, alone or in combination withother data, to obtain quantitative lung volume measurements to use inmanagement of the patient after surgery or in other situations. Inanother embodiment, the calibration coefficient is derived from lungvolume or flow measurements obtained on an intubated patient frommeasurements recorded from a mechanical ventilator.

Preferably the device is linked to a spirometer, ventilator orpneumotachometer to provide volume or flow calibration. Preferably, thedevice is linked to a spirometer or ventilator or pneumotachometer toprovide volume calibration. In one embodiment, the operator will run thepatient through a brief breathing test regimen of one or more of thefollowing: at least one tidal breathing sample, at least one forcedvital capacity (FVC) sample, at least one measurement of minuteventilation sample, and at least one maximum voluntary ventilation (MVV)sample. The device will be calibrated based on the results of thespirometer tests relative to the impedance measurements. In a preferredembodiment, calibration will be implemented from measurements takenduring tidal breathing. In particular, for patients who are unable tocomply with the procedure, a simple tidal breathing sample will betaken, which requires no coaching or compliance. The tidal breathingsample is collected over 15 seconds, 30 seconds, 60 seconds, or anothertime frame.

In one embodiment, a calibration coefficient for a given individual iscalculated based on combined spirometry and RVM data and applied todeliver an absolute volume measurement for RVM measurements taken at afuture time. Preferably, this absolute volume measurement will bevalidated or modified at the future time using calibration capabilitiesintrinsic to the hardware and current measurements derived from thedevice. In a preferred embodiment, an algorithm is applied to RVM databased on patient demographics, existing normal spirometry data forvarying patient demographics found in the work of Knudsen, Crapo, andothers and/or other anatomic or physiologic measurements to provide auniversal calibration to deliver absolute volume measurements withoutthe need for individual calibration with a spirometer or ventilator.

Preferably, the device may be used in conjunction with ECG or ICG datato produce further calibration of impedance data by utilizing parametersderived ECG and ICG such as heart rate and SNR. Preferably, ECG or ICGdata will help validate proper electrode placement. In anotherembodiment, the electrical activity of the heart is used to enhance thedevice calibration. Preferably the device can measure the followingcardiac, pulmonary and other physiology parameters and features: HeartRate (HR), baseline impedance, impedance magnitude, Pre-ejection Period(PEP), Left Ventricular Ejection Time (LVET), Systolic Time Ration(STR), Stroke Volume (SV), Cardiac Output (CO), Cardiac Index (CI),Thoracic Fluid Content (TFC), Systolic Blood Pressure (SBP), DiastolicBlood Pressure (DBP), Mean Arterial Pressure (MAP), Mean Central VenousPressure (CVP), Systemic Vascular Resistance (SVR), Rate PressureProduct (RPP), Heather Index (HI), Stroke volume Index (SVI), andWaveform Accuracy Value (WAV). Baseline values calculated from patientcharacteristics for these features are utilized to derive thecalibration coefficient as well as calculate an index of overallrespiratory sufficiency. Conversely, RVM data can be used to enhanceaccuracy or utility of ICG data such as Heart Rate (HR), baselineimpedance, impedance magnitude, Pre-ejection Period (PEP), LeftVentricular Ejection Time (LVET), Systolic Time Ration (STR), StrokeVolume (SV), Cardiac Output (CO), Cardiac Index (CI), Thoracic FluidContent (TFC), Systolic Blood Pressure (SBP), Diastolic Blood Pressure(DBP), Mean Arterial Pressure (MAP), Mean Central Venous Pressure (CVP),Systemic Vascular Resistance (SVR), Rate Pressure Product (RPP), HeatherIndex (HI), Stroke Volume Index (SVI), and Waveform Accuracy Value(WAV).

In particular, for patients who are unable to comply with a morecomplicated procedure, a simple tidal breathing sample of respirationsat rest is taken, which requires no coaching or compliance. Analysis ofthese data provides information relative to pulmonary physiology andrespiratory status that could not otherwise be obtained.

Referring now to FIG. 8, there is shown an impedance plethysmograph 31and a spirometer 32 both functionally connected to the same programmableelement 33. Volume data from the spirometer is preferably sampledsimultaneously or nearly simultaneously with the impedance reading ofthe impedance plethysmograph. Referring now to FIG. 9, there is shown apatient who is connected to a ventilator 34 as well as the impedanceplethysmograph 35, both functionally connected to a programmable element36. The volume of the ventilator is sampled simultaneously with theimpedance reading of the impedance plethysmograph. Referring now to thegraph in FIG. 10, there is shown a graph of volume versus impedance fora given patient undergoing various breathing maneuvers while data wassimultaneously collected using the impedance plethysmograph and aspirometer. The trace represented by FIG. 11 with volume over time isnormal breathing. The trace represented by FIG. 12 is slow breathing andthe trace represented FIG. 13 is erratic breathing. In one embodiment,the slope of the line of best fit 37 is used as the RVM calibrationcoefficient to compute volume from impedance. In another embodiment, analgorithm utilizing the slope, shape and/or other curve characteristicsand/or other demographic or body habitus characteristics of the patientis used to calculate the calibration coefficient.

In one embodiment a simple numerical value is obtained from a ventilatoror spirometer for tidal volume or minute ventilation for use incalibration of the device. One embodiment is comprised of a combinedsystem in which RVM and volume measurements are taken simultaneously,nearly simultaneously, or sequentially by means of a spirometer,pneumotachometer, ventilator or similar device and the combined datautilized to create an individual calibration coefficient for thecalculation of absolute volume from RVM measurements for a givenindividual.

EXAMPLE

One method of calibration has already been utilized in a small-scalestudy. Measurements of height, weight, chest circumference at maximuminspiration and normal expiration, distance from suprasternal notch toxiphoid, distance from under mid-clavicle to end of rib cage inmidaxillary line, distance from end of rib cage to iliac crest inmidaxillary line, and abdominal girth at umbilicus were taken andrecorded. Electrodes were positioned at the Posterior Left to Right,Posterior Right Vertical, and Anterior-Posterior, and ICG configurationdiscussed above. The four probes of the impedance measurement devicewere connected to the electrodes that corresponded to one of theconfigurations above. The ICG position was connected first and only usedto measure resting ICG of the subject in a supine position. The leadswere then reconfigured to connect to the Posterior Left to Rightposition. Once the leads were positioned correctly and the subject wassupine, the subject performed breathing tests which were measuredsimultaneously by the impedance measurement device and a spirometer fora sampling time of about 30 seconds. The breathing tests performed werenormal tidal breathing (3 runs), erratic breathing (2 runs), slowbreathing (2 runs), Forced Vital Capacity (FVC) (3 runs), and MaximumVentilatory Volume (MVV) (2 runs). FVC and MVV were performed accordingto ATS procedures. Normal, erratic, and slow tests were measured by abell spirometer, and FVC and MVV were measured by a turbine spirometer.Preferably, the calibration can be run all together on any type ofspirometer that meets ATS standards. Once all breathing tests werecomplete, the leads were repositioned to a new configuration, and thetests were run again until all configurations had been tested. The datawas collected on PC for the impedance data and turbine spirometer data,and on another PC for the bell spirometer data. The data was then mergedonto one PC and loaded into MATLAB. Preferably, MATLAB or other softwarepackages that utilize signal processing are used. Preferably, the datais loaded onto a PC or other computing station. Once the data wasmerged, the impedance and volume data from each breathing test werematched together using a GUI-based program. Correlation coefficients andcalibration coefficients were produced for each of the test runs bycomparing the impedance and volume traces using MATLAB. This data thenwas utilized in Excel to predict calibration coefficients based onpatient characteristics. Preferably, the data can be imported into andanalyzed in any software with a statistical package.

Referring now to FIG. 14, depicted is a graph of BMI versus thecalibration coefficient for 7 patients. BMI is shown on the x-axis, andcalibration coefficient is shown on the y-axis. The linear relationshipbetween height and the calibration coefficient in configuration D (PRRplacement as described earlier) is indicative of its utility indetermining the calibration coefficient. Other physiological parameterssuch as height weight, body surface area, race, sex, chestcircumference, inter-mammary distance, age also have importantrelationships with the calibration coefficient, and in one embodimentany or all of these parameters aid in accurate determination of thecalibration coefficient. A combination of statistical analysis and anexpert system is used to determine a given patient's correlationcoefficient based on the input of said physiological parameters. Suchmethods may include principal component analysis, artificial neuralnetworks, fuzzy logic, and genetic programming and pattern analysis. Ina preferred embodiment, test data from a pilot study is used to trainthe expert systems. In a preferred embodiment, existing data regardingpatient demographics and pulmonary function are used to train the expertsystem. Preferably, a combination of test data from a pilot study andexisting pulmonary function datasets are use to train the expert system.

One problem that is encountered with some spirometers is volume drift,where a greater amount of air is inspired rather than expired.Additionally, prolonged spirometry testing provides increase inresistance to pulmonary flow that can alter the physiology and/or canchange the respiratory flows and/or volumes. These patterns can disruptthe correlation coefficient for the test by altering the volume so thatit trends downwards while the impedance trace stays constant. FIG. 15shows a volume curve that exhibits volume drift. FIG. 16 shows a volumeversus impedance curve for that set where the volume drift damages thefit of the plot. In one embodiment, the device corrects for the problemby subtracting out a line with a constant slope value. After using thismean flow method, the curves do not trend up or down as seen in FIG. 17and the volume versus impedance data stays much tighter as seen in FIG.18, and the volume versus impedance data stays much tighter, givinghigher correlations and better correlation coefficients. In oneembodiment, volume drift subtraction is used in calibration. In oneembodiment volume drift subtraction is used in deriving the calibrationcoefficient. The same utility is also achieved by differentiating thevolume curve to get flow, subtracting the DC offset between intervalsthat have the same lung volume at the start and end point, and thenintegrating to get flow without the drift artifact.

In another embodiment of the device, the calibration coefficient isdetermined by comparing the RVM data trace and calculated valuescompared to predicted values for the patient's tidal volume, FVC, FEV1etc. based on standard tables of spirometric data created by Knudsen,Crapo, or others known to those skilled in the art.

Data Analysis

Referring now to FIG. 19, there is shown a flow chart that displays theprogression of data through the analysis software. Raw data is recordedby the impedance meter, digitized using an analog to digital converter,and inputted to the programmable element through a standard data port.Data processing strips the signal of noise and motion artifacts.Analysis algorithms calculate the volume trace as well as medicallyrelevant information including but not limited to: frequency and timedomain plots of the impedance and/or calculated volume traces,respiratory rate, tidal volume, and minute ventilation. In oneembodiment, the analysis algorithm to convert impedance into volumetraces utilizes either calibration in conjunction with spirometer orventilator data, or in another embodiment, calibration based onphysiological parameters. The algorithm produces a correlationcoefficient which, when multiplied with the impedance data, converts theimpedance scale into a volume scale. In addition, the algorithms takevariability of the above metrics into account and automaticallycalculate a standardized index of respiratory sufficiency (RSI). ThisRSI contains information that integrates information from one or moremeasurements and/or utilizes the range of acceptable values of thefollowing measurements individually and in combination to provide asingle number related to respiratory sufficiency or insufficiency:respiratory rate, respiratory volume, respiratory curve characteristics,respiratory variability or complexity as previously prescribed.

In one embodiment, one of the following methods are used in calculationof the RSI: change in patient status from previous measurement, secondderivative of change in patient status from previous measurements,multivariate analysis, pattern analysis, spectral analysis, neuralnetworks, self-teaching system for individual, self-teaching system forpatient population.

In one embodiment, the RSI also includes data from the following: oxygensaturation, TcpO2, TcpCO2, end tidal CO2, sublingual CO2, heart rate,cardiac output, oncotic pressure, skin hydration, body hydration, andBMI. The advantage of this index is that it can be understood byuntrained personnel and it can be linked to alarms to notify physiciansor other caregivers in case of rapidly deteriorating health. Aftercomputation, processed metrics pass to the output module, which may beembodied as a printer or displayed on a screen or delivered by oral,visual, or textual messaging.

In one embodiment, the device notes a pattern in the curve recordedduring the inspiratory or expiratory phase of respiration. In oneembodiment, the device notes a pattern in the respiratory variability inrate, volume and/or location of respiration. In one embodiment thepattern is noted in the shape of the respiratory curve. In oneembodiment, the pattern analysis includes the values derived from theslope of inspiration. In one embodiment, the pattern analysis includesthe values derived from the slope of expiration. In one embodiment, thepattern analysis includes a combination of parameters which couldinclude any or all of the following: respiratory rate, minuteventilation, tidal volume, slope of inspiration, slope of expiration,respiratory variability. In one embodiment, these parameters are usedwithin the calculation of a Respiratory Health Index (RHI) that providesa standardized quantitative measure of adequacy of ventilation. In oneembodiment, the RHI is coupled with alarms that sound either whenrespiration falls below what is deemed as adequate, or within the rangethat is deemed adequate, if the patient experiences a very suddenchange. In one embodiment, the device provides information to calculatean RHI. Preferably the device calculates and displays the RHI. In oneembodiment, the Respiratory Health Index is compared against a universalcalibration based on patient characteristics. In one embodiment, the RHIprovides quantitative data with the system calibrated to a specificpatient.

Referring now to FIG. 27, the time delay or phase lag of an impedancesignal and a volume signal is shown. In this particular figure, thedelay was found to be 0.012 seconds. Phase lag between volume andimpedance signals is an important issue that is addressed in oneembodiment. There is a time lag between impedance and volume signals dueto the elastic and capacitive nature of the pleura and lung tissue,which creates a slight delay between the diaphragm moving and airflowing in the lung. In one embodiment, this phase difference is used asa measure of lung stiffness and airway resistance. Frequency phaseanalysis allows the user to find the phase angle. A larger phase offsetis indicative of a high degree of airway resistance to motion.Calculation of the phase angle is accomplished by comparingsimultaneously recorded and synchronized RVM curves with flow, volume orpressure curves recorded by a spirometer, pneumotachometer, ventilatoror similar device. In one embodiment the phase lag between volume andimpedance signals is a component of the algorithm that is used tocalibrate the system to a given individual. In one embodiment the phaselag is used to calibrate the system for a universal calibration. Whencalculating the calibration coefficient using an external pressure,flow, or volume measuring device, the leading curve is shifted by themagnitude of the phase lag so as to correlate temporally with thetrailing curve. This embodiment increases the accuracy of thecalibration algorithm. When no external pressure, flow, or volumemeasuring device is used for calibration, a virtual phase lag iscalculated based on patient characteristics, including demographicinformation, physiological measurements, and pulmonary function testmetrics.

In one embodiment, phase lag is corrected for by RVM algorithms inaligning both impedance and volume. In one embodiment, phase lag data ispresented independently as a standardized index to demonstrate a measureof lung compliance and stiffness. In one embodiment, phase lag data isintegrated within the Respiratory Health Index as a measure ofrespiratory status.

In one embodiment, frequency domain analysis is applied to the RVMmeasurements. Preferably, at least one frequency domain plot such as aFourier transform is displayed to the operator. Preferably, at least one2-dimensional frequency domain image of the RVM data such as aspectrograph is displayed to the operator, where one dimension isfrequency and the other is time, and the magnitude of the signal at eachlocation is represented by color. Preferably, the frequency domaininformation is used to assess respiratory health or pathologies.Preferably, an alarm will alert a medical professional if the frequencydomain data indicates rapid deterioration of patient health.

In a preferred embodiment, RVM measurements are used as the basis forcomplexity analysis. In one embodiment, complexity analysis is performedon the RVM signal alone. Preferably, RVM measurements are used incombination with other physiologic measurements such as heart rate,urine output, EKG signal, impedance cardiogram, EEG or other brainmonitoring signal.

In a preferred embodiment, RVM measurements are utilized as a componentof complexity analysis in combination with data provided by a deviceused to treat or monitor the patient including: the ventilatormeasurement of the patient generated respiratory pressure, theventilator measurement of the patient generated respiratory flow, theventilator measurement of the patient generated respiratory volume, theventilator measurement of the ventilator generated respiratory pressure,the ventilator measurement of the ventilator generated respiratory flow,the ventilator measurement of the ventilator generated respiratoryvolume an infusion pump, or other devices used to treat the patient, RVMmeasurements may be used to quantify breath-to-breath variability. Oneembodiment of the device is used to define a specified point along therespiratory curve with which to calculate breath-to-breath variabilityin respiratory rate such as the peak of inspiration or nadir ofexpiration. Preferably, peaks or nadirs of each respiration areautomatically identified. In one embodiment, the device provides datawith describing breath-to-breath variability in volume inspired. In oneembodiment, the device provides data describing breath-to-breathvariability or complexity in the slope or other characteristics of therespiratory volume or flow curve. In one embodiment, the device providesdata with which to calculate variability or complexity associated withthe location of respiratory effort, such as chest vs. abdominal or onehemithorax vs. the other, by collecting data from different locations onthe body with the same or different electrode pairings. Preferably, thedevice calculates breath-to-breath variability or complexity of one ormore of these parameters. Preferably, the device presents thevariability or complexity analysis in a form that is easy to interpretby the user. In one embodiment, the device combines data from more thanone source of variability or complexity among the following: respiratoryrate, respiratory volume, location of respiratory effort, slope or othercharacteristic of the respiratory volume or flow curves, to provide anadvanced assessment of respiratory function. In one embodiment, thedevice analyzes the variability or complexity data intermittently orcontinuously and presents the data at intervals such as every 10minutes, every 30 minutes, or every hour. Preferably, the devicepresents the variability analysis in less than 10 minutes, less than 5minutes, less than 1 minute, or in near real time. In one embodiment,the variability or complexity of any of the respiratory parameters maybe quantified by linear or nonlinear analysis methods. Preferably, thevariability or complexity of any of the respiratory parameters may bequantified by nonlinear dynamical analysis. In one embodiment,approximate entropy is used by the device for data analysis. In oneembodiment, variability or complexity analysis of the data is combinedwith volume data to provide a combined index of respiratory function. Inone embodiment, variability or complexity analysis data is combined withother parameters and presented as a Respiratory Sufficiency Index or aRespiratory Health Index.

In a preferred embodiment, RVM measurements or the complexity analysisof the RVM signal is utilized as at least a part of the information usedin goal directed therapy. In a preferred embodiment, RVM measurements orthe complexity analysis of the RVM signal provide information fordecision support. In a preferred embodiment RVM measurements or thecomplexity analysis of RVM signal is utilized as at least a part of thepatient data required for a controlled loop system.

Use in Imaging

In one embodiment of the device, the respiratory cycle is measured byone or more methods including but not limited to impedance pneumography,end tidal CO₂, or pulse oximetry while the heart is imaged or otherwisemeasured using echocardiography which may be embodied as 2D echo, 3Decho or any other type of echocardiography. Time series data from theechocardiogram is marked as having a certain accuracy rating based onthe respiratory motion recorded by the respiratory monitor. In oneembodiment, echocardiography data below an accuracy threshold isdiscarded. In another embodiment, echocardiography data is weightedbased on its accuracy rating where the least accurate data is weightedlowest. The device generates a composite image or video of the heart andcardiac motion based on the most accurate echocardiogram data. In oneembodiment, echocardiography data is recorded over more than one cardiaccycle, then after analysis and accuracy rating, the best data is usedfor generating a composite image of the heart or video of the cardiaccycle.

Other embodiments include combining respiratory cycle measurement andquantification with other cardiac imaging techniques for the purpose ofimproving accuracy. The methods of cardiac imaging may include Dopplerflow measurements, radionuclide study, gated CT, and gated MRI. Otherembodiments include combining respiratory cycle measurement by RVM withother diagnostic or therapeutic modalities of the chest, abdomen, andother body parts, including diagnostic CT or MRI, catheter directedtherapy, directed cardiac ablation, radiofrequency ablation of tumor,radiation of tumor. In a preferred embodiment, RVM and cardiac impedancedata are utilized together for timing of data collection or dataanalysis of diagnostic imaging or anatomically directed therapy.

In another embodiment of the device, the respiratory impedancemeasurements or data from complexity analysis of RVM measurements areused to generate an image of the lungs. In another embodiment of thedevice, data from complexity analysis of RVM measurements and cardiacimpedance measurements are used to generate an image of the heart andlungs. In the preferred embodiment, the heart and lungs are imagedsimultaneously. In one embodiment, the device is used for generating 2Dimages, videos, or models of the heart and/or lungs. In the preferredembodiment, the device generates 3D images, videos or models of theheart and/or lungs.

Detecting Pathologies and Improving Monitoring

In one embodiment, the device provides RVM data which, with our withoutvariability or complexity analysis, is used to aid in decision makingsuch as extubation or intubation for mechanical ventilation. In oneembodiment the device provides RVM data which, with or withoutvariability or complexity analysis, aids in decision making regardingdrug administration or other therapeutic intervention. In oneembodiment, the device uses variability or complexity information aloneor with volume data as part of an open or closed loop control system toadjust ventilatory settings. In one embodiment, the device usesvariability or complexity information, alone or with volume data orother analysis of the respiratory curve provided by RVM, as part of anopen or closed loop control system to adjust doses of medications. Thisembodiment is useful for premature infants to optimize the management ofa pressure ventilator, and for patients with uncuffed endotrachealtubes. In one embodiment, the device uses variability or complexityinformation, alone or with volume data or other analysis of therespiratory curve provided by RVM, as part of a patient managementsystem that monitors patient status, recommends medication delivery,and, then, reassesses the patient to direct further action.

In one embodiment the device uses variability or complexity analysis ofthe RVM signal alone, volume data alone, curve analysis alone, or any ofthese in combination to trigger alarms indicating change in patientstatus. In another embodiment, symbol-distribution entropy andbit-per-word entropy are used to measure the probability of patternswithin the time series. In another embodiment, similarity ofdistributions methodology is used. In one embodiment, the device soundsan alarm when it detects a change in respiratory complexity or arespiratory complexity below a specified threshold or more constrainedbreathing patterns associated with pulmonary pathology or diseasestates. In one embodiment, the device sounds an alarm when it detects achange in a combined measurement of respiratory and heart ratecomplexity beyond a specified threshold.

In one embodiment, RVM measurements are integrated into an open orclosed feedback loop to report adequacy of ventilation by ensuring safedosage of medication by monitoring ventilation for warning signs ofrespiratory arrest. In a preferred embodiment, RVM is integrated into asystem with a ventilator providing an open or closed feedback loop bywhich ventilator adjustments are made. Differences between RVMmeasurements and ventilator or spirometer generated volume or flowmeasurements can be used to provide information for diagnosis andguidance of therapy. By using RVM monitoring with or without additionalinformation from end tidal CO₂ or pulse oximetry measurements, thisembodiment automatically weans the patient by gradually decreasingventilatory support and observing RVM and other parameters and alertsthe physician of readiness for extubation, or alerts for failure toprogress. The system may additionally include machine intelligence inthe form of supervised and unsupervised learning based on patient's ownand/or population-based data. Preferably the system is able to provideclinical guidance and suggestions for ventilator-regulation use.

This combined system with either pulse oximetry or ETCO2 or both couldbe used as an open or closed loop system to deliver narcotics or otherrespiratory depressant drugs such as benzodiazepines or propofol.

In one embodiment, the analysis algorithm detects the presence ofspecific respiratory patterns maintained in the expert system databaseand informs the physician or other health care provider about thepossibility of associated pathology. In one embodiment, the respiratorypattern for a given pathology is recognized and in a preferredembodiment, quantified. In another embodiment the pathology islocalized.

In a preferred embodiment, the device recognizes a specific patternsrelated to respiratory volume, curve, variability or complexity or otheranalysis of RVM data.

In one embodiment, the device recognizes the pattern associated withimpending respiratory failure or respiratory arrest and delivers anaudible and/or visible alert or warning. In one embodiment, the deviceanalyzes the respiratory data or the trend in the data and makes arecommendation for intubation and mechanical ventilation. In oneembodiment, the device analyses the respiratory pattern data and adjuststhe level of infusion of a narcotic or other respiratory depressant drugsuch as propofol.

In one embodiment, the device recognizes the respiratory patternassociated with a specific disease entity or pathology such ascongestive heart failure, or asthma or COPD or narcotic inducedrespiratory depression or impending respiratory failure. In oneembodiment, the device alerts the physician to this pathology. In oneembodiment the device quantifies the degree of the pathology. In oneembodiment, the device recognizes a pattern of congestive heart failureand provides data regarding the trending toward improvement ordeterioration with time or as associated therapeutic intervention.

Preferably, the impedance measuring element of the device can produceImpedance Cardiograph (ICG) measurements. Preferably, the device detectsimpedance variability associated with heart rate variability. Preferablythe device detects impedance variability associated with variability ofthe respiratory waveform or other respiratory parameter and utilizes theheart rate and respiratory rate, volume or waveform variability topredict cardiac, respiratory and pulmonary complications. Preferably,the device maintains alarms for predetermined limits associated withunsafe pulmonary variability or complexity or combined heart rate andrespiratory variability or complexity.

In another embodiment, End Tidal CO₂ (ETCO₂) is used in addition to orinstead of subjective assessment to determine the RVM baseline. In oneembodiment, RVM is coupled with ETCO₂ measurements to provide additionalinformation regarding respiratory status.

In another embodiment RVM is coupled with pulse oximetry to provideinformation about both ventilation/respiration and oxygenation. A morecomplex RVM system couples standard RVM measurements with both or eitherETCO₂ or pulse oximetry. This combined device provides furtherinformation about breathing for sedated patients and enhances patientmonitoring.

In a preferred embodiment, measurements of lung volumes and minuteventilation are used to assess the adequacy of the patient afterextubation in a quantitative way. Minute ventilation is specificallyused for patients undergoing surgery. Preferably, a preoperativemeasurement of tidal volume or minute ventilation is obtained as abaseline for the specific patient. Preferably the baseline is usedpost-operatively as a comparison between preoperative and postoperativerespiratory status. The trend of tidal volume or minute ventilation isused to monitor a patient during surgery or a procedure or duringpost-operative recovery in the Post Anesthesia Care Unit, in theIntensive Care Unit, or on the hospital floor. This trend gives anaccurate measure of differences and changes in the patient's breathingfrom pre-procedure baseline and can denote when the patient returns to abaseline level of breathing. In a preferred embodiment, the devicedirectly aids the physician to make an appropriate extubation decisionby defining an adequate level of breathing specific to that patient. Inone embodiment, absolute lung volumes are compared with pre-calibrateddata derived from patient characteristics, and are used in determiningthe presence of restrictive and/or obstructive lung disease and otherrespiratory conditions. Absolute volume data can be especially usefulwithin the PACU and ICU as a complement to existing quantitative data.

The system is preferably capable of monitoring patient's respiratorystatus before and after extubation, providing recommendations foradditional respiratory treatment or medications (if necessary) orindicating that there is no need for further treatment and the patientis ready for transfer off of mechanical ventilation, CPAP, BiPAP, orHigh-flow O2. The system is preferably capable of detecting smallbreaths that may not be otherwise detected by a ventilator. Preferably,the system may be able to provide an extubation trial before actualextubation, while monitoring data to support the extubation.

In one embodiment, the system preferably provides an indication of theneed to intubate or re-intubate a patient. The indication may be audibleand/or visual. In another embodiment, the system preferably controlsexternal ventilation and respiratory treatment or therapy via eitheropen and close loop based on the RVM measurements.

In another embodiment, the system preferably performs real-time analysisof shape of the expiratory and inspiratory impedance or tidal volumesignal curve to determine at least one of: readiness for extubation,need for intubation, need for re-intubation, and need for additionaltreatment. In another embodiment, the system preferably providesreal-time feedback and control of the ventilator to prevent damage tothe lungs from over distention of the alveoli, resulting from eithermechanical ventilation (VILI) or spontaneous ventilation (SILI) or toprevent damage through excessive driving pressure.

In another embodiment, the system will preferably perform real-timeanalysis of the flow-volume loops, specifically the hysteresis in thoseloops, to determine at least one of: readiness for extubation, need forintubation, need for re-intubation, need for additional treatment. Inanother embodiment, the system will preferably provide real-timefeedback to prevent damage to the lungs from over distention of thealveoli, resulting from either mechanical ventilation (VILI) orspontaneous ventilation (SILI) or to prevent damage through excessivedriving pressure. In another embodiment, the system preferably providesreal-time feedback identifying Atelectasis, the collapse or closure ofthe lung in which the alveoli have little or no volume.

Use in PCA feedback and Drug Dosing Optimization

One use of the device is to use cardiac and/or respiratory data measuredand recorded by one, several, or a combination of the technologieslisted herein, to determine the effect of one or more drugs or othermedical interventions on the patient. In an embodiment, the respiratorymonitor is used to judge the side effects of analgesic drugs on the bodyand prevent or assist in the prevention of respiratory failure or othercompromises due to adverse reaction or overdose.

In a preferred embodiment, the device is paired with or integrated intoa patient controlled analgesia (PCA) system. This is accomplishedelectronically through communication between the device of the inventionand an electronic PCA system, or by an integrated monitor/PCA system orby a setting in the monitor indicating that the patient is beingadministered PCA. In this embodiment, the administration of analgesia oranesthesia is limited based on the risk of respiratory or othercomplications predicted by the device. If the PCA system is notelectronic, or analgesic drugs are being delivered by personnel, thedevice makes recommendations as to when the risk of respiratorycomplication is high and the dosage should be lowered.

Another embodiment of the device of the invention is adiagnostic/therapeutic platform. The monitoring device is paired withone or more of the following: pharmaceutical regimens, therapeuticregimens, use of inhaler, use of nebulizer, use of pharmaceuticaltargeting respiratory system, use of pharmaceutical targetingcardiovascular system, use of pharmaceutical targeting asthma, COPD,CHF, cystic fibrosis, bronchopulmonary dysplasia, pulmonaryhypertension, other diseases of the lungs. This embodiment of the deviceis used to judge the effectiveness of possible medical and nonmedicalinterventions on respiratory state or respiratory health and suggestchanges in regimen for optimization and/or suggest appropriateinterventions when the patient is at risk for complications.

In one embodiment RVM is paired with behavioral algorithms or algorithmthat includes information about any of the following patient medicalstatus, environmental factors, and behavioral factors of a demographicgroup or of the patient in general. In a preferred embodiment, one ofthe algorithms described above could denote the necessity for obtainingan RVM measurement. More preferably, the RVM measurements are used inconjunction with behavioral/medical/environmental algorithmic data toprovide information to indicate action or therapy. An example of the useof this embodiment of the device would be an algorithm which includesthe patient's previous respiratory complications or chronic respiratoryillness, and/or allergies as inputs along with behavioral events knownto exacerbate said conditions. By including information from thepatient's schedule (e.g. attending an outdoor event during allergyseason, or participating in a sporting competition), the systemrecommends that he take an RVM measurement then makes recommendationsabout whether to maintain normal dosing of medication or increase it.The software can also recommend that the patient bring medication withhim to the event, and generally remind the patient to take hismedication. Another example could be that the patient had an asthmaattack or other respiratory complication. RVM data could be utilized toassess the severity of this attack by any of the measured parametersincluding minute ventilation, tidal volume, time for inspiration vs.expiration (i.e. ratio), shape of the respiratory curve during normalbreathing, shape of the respiratory curve during the deepest possiblebreath or other respiratory maneuver. The data could then promptindependently or be used in conjunction with other information to make adecision for the patient to perform an action including one of thefollowing: do nothing, rest, use an inhaler, take a pharmaceutical, usea nebulizer, go to the hospital. Information as to the action requiredcould be part of a behavioral or other algorithm designed for thespecific patient or a group of patients with a similar disorder,patients with a similar demographic, patients with a specific medical,anatomic or behavioral profile or patients in general. Preferably, afterthe action, the patient is instructed to repeat the RVM measurement toassess the adequacy of therapy. Preferably his repeat measurement iscompared to the measurement before the therapy or other intervention andchanges are noted. Additional information from this comparison or justdata taken after therapy is used alone or in combination with otherpatient data to make further medical decisions or recommendations foraction.

For example, an asthmatic is having symptoms and decides to or isinstructed by a disease management algorithm to obtain an RVMmeasurement. The RVM data is analyzed by the device, utilizedindependently or compared to his historic baseline or the lastmeasurement taken. Based on these, with or without other patientspecific inputs such as heart rate, the device recommends he use hisinhaler. A second set of RVM data is then taken. The RVM data iscompared to the previous RVM data taken prior to treatment. The devicethen follows a decision tree and tells the patient he has improved andneeds no further therapy, that he needs to repeat the dosage, that heneeds to call his physician, or that he immediately needs to go to thehospital. In a preferred embodiment, the RVM data is combined withbehavioral algorithms developed for a demographic or for a specificpatient to optimize recommendations for the patient.

PACU/ICU Usage

In one embodiment, the device is used within a Postoperative AnesthesiaCare Unit (PACU) setting, as either a standalone monitor or as anaccompaniment to or incorporated in an existing monitor. Within thePACU, RVM volume is calculated and compared against pre-calibrated dataderived taking into account BMI, height, weight, chest circumference,and other parameters. The device is used to complement existingquantitative data that supports decision making within the PACU. In oneembodiment, within the operating room, RVM data is correlated with endtidal carbon dioxide measurements to provide a more comprehensiveassessment of respiratory status. RVM derived measurements includingminute ventilation are used to compare a patient's status before,during, and after surgery or a procedure and to document the effect ofanesthesia/narcotic induced respiratory depression. RVM is used tosupport more subjective assessments made by clinicians in the PACU byproviding a quantitative justification for certain decisions, includingthe decision to re-intubate. The device also supports subjectiveassessment regarding patients on the hospital floor as a monitor fordecline in respiratory status and an alarm for the need to re-intubateor perform another intervention to improve respiratory status.Preferably, RVM measurements will assist in regulation of narcotic painmedication, sedative drugs such as benzodiazepines, or other drugs withrespiratory depressive effects. In one embodiment, the above mentioneduses regarding the RVM in a PACU setting are implemented within the ICUsetting such as a Neonatal ICU, Surgical ICU, Medical ICU, PulmonaryICU, Cardiac ICU, Coronary Care Unit, Pediatric ICU, and NeurosurgicalICU. In another embodiment, the RVM device is used in the setting of astep down unit or standard hospital bed to follow respiratory status.

Later in the postoperative period or otherwise, measurements of therespiratory pattern, including tidal volumes, respiratory rate, minuteventilation, variability in inter-breath interval or volume, or RVMsignal complexity can be compared to baseline values measured beforesurgery. This can directly aid the extubation decision by defining whatis an adequate level of breathing specific to that patient. In anotherembodiment of the device, RVM monitoring identifies problems that arecommonly associated with ventilators, such as poor endotracheal tubepositioning, hyperventilation, hypoventilation, rebreathing and airleaks. The system also identifies air leaks through a chest tube orcuffless tube. Air leaks would cause a downward trend to appear on anydirect volume measurement which would not be present on the impedancetrace, thus the device can detect and report air leaks in devices whichdirectly measure volume or flow. In a preferred embodiment, the systemidentifies abnormalities and trends specific to a hemithorax such asthose related to the following pathologies: pneumothorax, pulmonarycontusion, rib fractures, hemothorax, chylothorax, hydrothorax, andpneumonia.

In one embodiment, the device is used during Monitored Anesthesia Care(MAC) to monitor respiratory status, assist in drug and fluidadministration, provide indication of impending or existing respiratorycompromise or failure, and assist in the decision to intubate ifnecessary.

In another embodiment of the device, RVM monitoring identifies problemsthat are commonly associated with ventilators, such as poor endotrachealtube positioning, hyperventilation, hypoventilation, rebreathing and airleaks. In one embodiment RVM measurements are combined with data derivedfrom the ventilator to provide additional data regarding physiology. Anexample of this is that differences can be recorded in RVM measurementsvs. inspired or expired flows or volumes measured on the ventilators toassess “work of breathing” in a quantitative fashion.

In another embodiment, RVM measurements are taken after surgery in apatient who is still under the effects of anesthesia or pain medicationto monitor patient recovery. Recording a baseline tidal volume curve fora patient during normal preoperative conditions provides a comparisonbaseline for monitoring during and after surgery. Returning to a similartidal volume curve is one signal of respiratory recovery after beingtaken off a ventilator. In this embodiment of the invention, the deviceis used to evaluate the success of extubation and determine ifreintubation is necessary. The invention described herein allows thesemeasurements to be taken noninvasively and without being in the streamof inspired/expired air or impeding airway flow or contaminating theairway circuit.

In one embodiment, the device is used within outpatient surgicalcenters, specifically geared towards patients receiving MonitoredAnesthesia Care, including patients undergoing orthopedic procedures,cataract surgery and endoscopy of the upper and lower GI tract.

Diagnostic Usage

In one embodiment, the device is used to quantify respiratory parametersduring performance based tests. In a preferred embodiment, the device isused to quantify respiratory parameters in tests of cardiovascularfunction including stress tests. In a preferred embodiment, the deviceis used in combination with one of the following tests to assess impactof the test on respiration. In a preferred embodiment, the devicereports effects of exercise or a particular drug like dopamine on theoverall physiology or metabolism of the body as reflected by changes inrespiratory volumes, patterns, rate or combinations thereof includingadvanced analysis of breath-to-breath variability/complexity, fractal orentropy based analyses as described elsewhere. In a preferredembodiment, the device is used to evaluate the safety of a given levelof exercise or pharmacologic stress.

In a preferred embodiment, variability or complexity analysis of RVMmeasurements is undertaken in concert with standard pulmonary functiontesting. In a preferred embodiment, variability or complexity analysisof RVM measurements is undertaken with or without heart ratevariability/complexity analysis in concert with standard cardiovascularphysiology testing such as stress testing, walking tests forclaudication, or other performance based testing.

In a preferred embodiment, the device is used to evaluate the effects ofdrugs on the respiratory system including bronchodilators for diagnosticpurposes, monitoring of therapeutics, optimization including effects onboth heart and lungs. More preferably, the device above combinesrespiratory information obtained by impedance or other methods describedwith EKG information about heart rate, heart rate variability, EKGevidence of ischemia or arrhythmia. In a preferred embodiment, thedevice is used to evaluate the effects of bronchoconstrictors as in aprovocative test. In various embodiments, the device obtains continuousor intermittent RVM measurements. In a preferred embodiment, the deviceprovides trending of RVM data.

In a preferred embodiment, the device is used to evaluate the effects ofmetabolic stimulants, cardiovascular drugs including beta blockers,alpha adrenergic agonists or blockers, beta adrenergic agonists orblockers. In a preferred embodiment, the device is used during a stresstest to demonstrate level of effort placed or to demonstrate an unsafecondition relative to the pulmonary system to terminate or modify thetest. Stress Introduced to the patient is created by various meansincluding but not limited to, exercise and/or the delivery of a drug. Ina preferred embodiment, the device indicates or works with othertechnologies described earlier to indicate the level of overallexercise. In a preferred embodiment, the device is used as afree-standing device for measuring the effects of exercise or otherstimulant on the pulmonary system.

In another embodiment of the device, the respiratory information iscombined with cardiac information to define the level of exertionrelated to EKG changes associated with cardiac disease. In anotherembodiment of the device, the system combines respiratory informationwith cardiac information to determine the level of exertion of anathlete.

In another embodiment, the device provides warning of potential negativeimpact of the level of exercise on overall health or on cardiac status,with or without pairing respiratory signals with cardiac impedance orEKG measurements in the home, athletic field, military environment orout of hospital setting. One embodiment of the device is a holtermonitor which outputs values for one or more of the following:respiratory effort, level of activity, state of physiology, ormetabolism associated with different rhythms, depolarization or othercardiac pathophysiology.

One embodiment of the invention is similar to a holter monitor whichmonitors one or more physiological parameters over hours to days in ahospital, home, or other setting. One embodiment of the device iscombined with a holter monitor or critical care monitor whichspecifically monitors decompensation effects related to heart failure. Asimilar embodiment of the device monitors and outputs measurements of“lung water”. In one embodiment, the device is included in a diseasemanagement system for congestive heart failure.

In a most preferred embodiment, the device provides a continuousmeasurement which can be run for long periods of time and can deliver atime curve demonstrating the effects of exercise or a drug fordiagnosis, therapeutic monitoring or drug development.

One embodiment of the device provides trending data over minutes tohours to days for patients with a variety of disease states includingchronic obstructive pulmonary disease, congestive heart failure,pulmonary hypertension, pulmonary fibrosis, cystic fibrosis,interstitial lung disease, restrictive lung disease, mesothelioma, postthoracic surgery, post cardiac surgery, post thoracotomy, postthoracostomy, post rib fracture, post lung contusion, post pulmonaryembolus, cardiac ischemia, cardiomyopathy, ischemic cardiomyopathy,restrictive cardiomyopathy, diastolic cardiomyopathy, infectiouscardiomyopathy, hypertrophic cardiomyopathy. Preferably the deviceprovides information about changes in respiration in these diseasestates related to interventions or provocative testing procedures.

In one embodiment of the device of the invention, the system is used todiagnose various diseases. In a preferred embodiment, the device is usedto assess the risk of developing pneumonia. In another embodiment, thedevice is used to assess the risk that a pneumonia therapy is noteffective, and suggest corrective action. Another embodiment of theinvention is used for the evaluation of functional deterioration orrecovery associated with diseases including but not limited to:pneumonia, heart failure, cystic fibrosis, interstitial fibrosis,hydration levels, congestion due to heart failure, pulmonary edema,blood loss, hematoma, hemangioma, buildup of fluid in the body,hemorrhage, or other diseases. This information may be used fordiagnosis as above or be integrated with respiratory volume measurementsor other physiological measurements that may be measured by the deviceor input into the device to provide a comprehensive respiratorysufficiency index (cRSI).

In one embodiment, disease specific modules can be created to gatherdisease specific information, employ disease specific algorithms anddeliver either optimized respiratory volume data or respiratorydiagnostic data related to the specific disease. In a preferredembodiment of the invention, respiratory curve analysis is used todiagnose medical conditions. In one embodiment, the system utilizesprovocative tests to determine measurements or estimates of one or moreof the following: tidal volume, residual volume, expiratory reservevolume, inspiratory reserve volume, inspiratory capacity, inspiratoryvital capacity, vital capacity, functional residual capacity, residualvolume, forced vital capacity, forced expiratory volume, forcedexpiratory flow, forced inspiratory flow peak expiratory flow, andmaximum voluntary ventilation. In this embodiment, diagnostic tools suchas flow volume loops are generated by software running on the system fordiagnosis of various cardiopulmonary or other disorders.

Respiratory curve analysis can also be used to assess cardiopulmonary orother disorders without provocative tests. In one embodiment, analgorithm monitors trends in TV, MV and RR to provide a metric ofrespiratory sufficiency or respiratory sufficiency index (RSI). Inanother embodiment, an algorithm analyzes individual breaths as an inputto diagnose respiratory conditions. In this embodiment, one or more ofthe following parameters are calculated on a breath by breath basis:inspiratory time (I_(t)), expiratory time (E_(t)), I_(t):E_(t) ratio,percent inspiratory time, tidal impedance, tidal volume and area underthe curve. In this embodiment, the various parameters are outputtedthrough the system's user interface or printable report for the user toassess respiratory disease state. In a preferred embodiment, analgorithm analyzes the parameters to act as a diagnostic aid. In thisembodiment, the system outputs an index of disease severity or apositive/negative reading for the disease.

In one embodiment, the device is implanted. In a preferred embodiment,the device is powered from a pacemaker-like battery. In one embodimentthe device is combined with a pacemaker or defibrillator. In oneembodiment the device is adjusted or calibrated or interrogated using anexternal component.

FIG. 40 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a High-Frequency ChestWall Oscillation (“HFCWO”) vest. It has recently been observed thatduring vest oscillation therapy, the Minute Ventilation of a patient isreduced by up to 50%. The improvement in efficiency may providesignificant health benefits for a patient who is having difficultyproviding oxygenation of their bloodstream during breathing. In apreferred embodiment, the HFCWO vest automatically provides therapylevels (frequency, intensity, length) which have been developed tooptimize the O2 to CO2 transfer in the lungs. The goal is to optimizethe oxygen and CO2 transfer by the use of the HFCWO vest. By increasingthe turbulence in the lungs during inhalation and exhalation betteroxygen and CO2 transfer can be achieved. Preferably, a decrease in workof breathing decreases the chance of respiratory failure. In addition,patients who are receiving oxygen therapy could combine the oxygentherapy with the HFCWO vest therapy to maximize oxygenation, improve CO2removal and decrease work of breathing, thereby preferably extendinglife.

Typically, HFCWO vest therapy provides for a 10 min treatment toeliminate exudate. The use of this product preferably allows for betteroxygenation. The use of the product could be continuously up to 24hrs/day. The system could be customized to activate when the patientrequires the additional oxygenation efficiency, e.g. during active timessuch as walking. As opposed to exudate removal the parameters ofoscillation could be optimized to minimize patient discomfort whilemaximizing oxygen transfer in the lungs.

As shown in FIG. 40, a sensor for acquiring a physiologicalbioelectrical impedance signal from the patient is preferablyfunctionally connected to a computing device. The computing devicepreferably analyzes the physiological bioelectrical impedance signal,and provides an assessment of minute ventilation and tidal volume of thepatient based on the analyzed bioelectrical impedance signal. Thecomputing device also preferably monitors the signal over time andprovides a signal to the HFCWO vest.

Preferably, the HFCWO vest automatically adjusts therapy levels(frequency, intensity, length) based on the levels of physiologicparameters including tidal volume, minute ventilation, and respiratoryrate during therapy as determined by the computing device. In addition,the general session-to-session lung performance can be tracked (TV, RR,MV) to demonstrate effectiveness of the therapy and the need to extendor modify the therapy levels. The goal is to optimize the oxygen and CO2transfer by the use of the HFCWO vest to increase the turbulence in thelungs during inhalation and exhalation.

In addition, the shape of the bioimpedance exhalation/inhalation curvecan be an indicator of the success of the therapy. Appropriate curvesfor maximizing oxygen transfer can be identified and the levels of theHFCWO vest (frequency, intensity, length of therapy, Baselinecompression) can be adjusted to get the desired respiratory curve andnecessary oxygenation and/or CO2 extraction and to minimize the work ofbreathing.

Additionally a pulse oximeter can be added to the system as an indicatorof the success of the enhanced compression therapy and improvedoxygenation. The levels of therapy can be optimized by watching theoxygenation response over time. CO2 monitoring can be added to thesystem with either end tidal or transcutaneous CO2 monitoring. Inaddition patients who are receiving oxygen therapy could combine theoxygen therapy with the HFCWO vest therapy to preferably maximizeoxygenation, improve CO2 removal, decrease work of breathing, and extendlife.

FIG. 41 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a mechanical ventilationtherapy device. The mechanical ventilation therapy device may be a CHFOsystem, a ventilator, a CPAP, a BiPAP, a CPEP (Continuous PositiveExpiratory Pressure), High-flow O₂ device, or another non-invasiveventilation device. Preferably, the system includes a sensor foracquiring a physiological bioelectrical impedance signal from a patientand is functionally connected to a computing device. The computingdevice preferably analyzes the physiological bioelectrical impedancesignal and outputs an assessment of minute ventilation and tidal volumeof the patient based on the analyzed bioelectrical impedance signal. Thesystem may also monitor the signal over time and provide a signal to themechanical ventilation device. The mechanical ventilation devicepreferably causes better oxygenation efficiency in the lungs. Themechanical ventilation device preferably can adjust the frequency,intensity, of the oscillations and/or the base line inhalation andexhalation pressures.

A bioelectric feedback signal provides indication for the success ofoxygenation. The characteristic values for tidal volume, minute volume,and respiratory rate will change. By monitoring the change, the systemcan automatically adjust the mechanical ventilation device's parametersto optimize physiological response and the efficiency of the system.Additionally a pulse oximeter can be added to the system as an indicatorof the success of the mechanical ventilation therapy. Improvedoxygenation and CO2 transfer can preferably be achieved or a decrease inwork of breathing can preferably be achieved to decrease the chance ofrespiratory failure. The levels of therapy can be further optimized bywatching the oxygenation response over time. In addition, the overalllength of therapy can be adjusted. The general session-to-session lungperformance can be tracked (TV, RR, MV) to demonstrate effectiveness ofthe ventilation and the need to extend or modify the therapy levels.

In addition, the characteristic shape of the bioimpedance inhalation andexhalation curve is an indicator of the success of the therapy. Bytailoring the therapy to get the desired expulsion curve, the system canoptimize oxygenation efficiency. Appropriate curves for maximizingventilation can be determined and the adjustment levels of theVentilator (frequency, intensity, length of therapy, Baseline Pressure)can be adjusted to get the desired respiratory curve. In addition,patients who are receiving oxygen therapy could combine oxygen therapywith mechanical ventilation therapy to maximize oxygenation and extendlife. Additionally, the level of compliance to using the system andgetting the adequate therapy can be monitored by analyzing the volume ofair coming in and out of the lungs.

By using the Tidal Volume, MV, and RR the relative success of opening upthe airways can be determined.

Mechanical ventilation therapy can be combined with aerosol delivery toprovide an additional therapy regimen. As the aspiration of aerosol willinherently modify the impedance characteristic of the lung, the level ofrespiration and the effect of these two combined treatments can also beoptimized. For example during the treatment the Tidal Volume and thecharacteristic inhalation and expulsion curves can be monitored before,during, and after treatment to ensure appropriate optimization of thepositive expiratory pressure on expansion of the lung and airways or anadequately cleared lung.

FIG. 42 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with an oxygenation therapydevice. The system preferably includes a sensor for acquiring aphysiological bioelectrical impedance signal from a patient and isfunctionally connected to a computing device. The computing devicepreferably analyzes the physiological bioelectrical impedance signal andprovides outputs an assessment of minute ventilation and tidal volume ofthe patient based on the analyzed bioelectrical impedance signal. Thecomputing device additionally preferably monitors the signal over timeand provides a signal to an oxygen therapy system. Preferably, theoxygen therapy provides oxygen via a mask or nose cannula. Thebioelectric feedback signal provides indication for the success of thelevel of the expansion of the airways. The characteristic shape of thebioimpedance expansion curve is an indicator that the air is gettinginto the lungs.

By combining the pressure monitoring of the inhalation and exhalationwith the impedance signal, the oxygenation therapy system cansynchronize the delivery of oxygen to the cannula to ensure optimaloxygen uptake through the nose cannula.

For oxygen therapy using a mask, the feedback mechanism of the oxygendelivery can be optimized as well. In addition, by using both theimpedance signal as well as the mask pressure, the oxygen system canmore reliably determine how well the mask is applied to the patient andhow well the circuit is maintained (kink free and leak free).

FIG. 43 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a suction therapy device.The system preferably includes a sensor for acquiring a physiologicalbioelectrical impedance signal from a patient and is functionallyconnected to a computing device. The computing device preferablyanalyzes the physiological bioelectrical impedance signal and providesan output of an assessment of minute ventilation and tidal volume of thepatient based on the analyzed bioelectrical impedance signal. Thecomputing device preferably also monitors the signal over time andprovides a signal to the suction therapy device.

Suction therapy preferably causes the mobilization of fluid in thelungs. The suction therapy can be adjusted for frequency and intensityof the oscillations. Also, the base line inhalation and exhalationpressures can be adjusted and the overall length of therapy can beadjusted.

The bioelectric feedback signal preferably provides an indication forthe success of the mobilization of secretions. As the suction drawsfluid, the characteristic values for tidal volume, minute volume, andrespiratory rate will change. By monitoring the change, the system canpreferably automatically adjust the suction parameters to optimizephysiological response.

In addition the characteristic shape of the bioimpedance expulsion curveis an indicator of the success of the therapy. By tailoring the therapyto get the desired expulsion curve the system can optimize themobilization of fluid from the patient.

Fluid clearance can be combined with aerosol delivery to provide anothertherapy regimen. As the aspiration of aerosol will inherently modify theimpedance characteristic of the lung, the level of respiration and theeffect of these two combined treatments can also be optimized. Forexample during the treatment the tidal volume and the characteristicinhalation and expulsion curves can be monitored before, during, andafter treatment to ensure appropriate outcome of an adequately clearedlung.

FIG. 44 depicts an embodiment of the invention wherein the impedancemeasuring device is in data communication with a cough assist device.The system preferably includes a sensor for acquiring a physiologicalbioelectrical impedance signal from a patient and is functionallyconnected to a computing device. The computing device preferablyanalyzes the physiological bioelectrical impedance signal and providesan output of an assessment of minute ventilation and tidal volume of thepatient based on the analyzed bioelectrical impedance signal. Thecomputing device preferably also monitors the signal over time andprovides a signal to the cough assist device.

The cough assist device is preferably a non-invasive therapy thatstimulates a cough to remove secretions in patients with compromisedpeak cough flow. It is designed to keep lungs clear of mucus. Retainedsecretions collect in the lungs, creating an environment for infection.Mechanical Insufflation/Ex-sufflation (MI/E) therapy products areimportant for patients who have weakened cough and are unable to removesecretions from the large airways without assistance. The systemsupplies positive pressure (inhale) to inflate the lungs, then quicklyshifts to supply negative pressure (exhale), during this processsecretions are sheared and can be expectorated or removed with suction.After the exhale, the system pauses and maintains a resting positivepressure flow to the patient. A facemask or mouthpiece can be used onendotracheal and tracheostomy (i.e. for patients with an appropriateadapter).

Preferably, the cough assist device automatically adjusts characteristictherapy levels (frequency, intensity, length of therapy, inhalationpressure, exhalation pressure) based on the levels of tidal volume,minute ventilation, and respiratory rate during therapy. In addition,the general within session and the session-to-session lung performancecan be tracked to demonstrate effectiveness of the therapy (before,during, and after and across many sessions). Graphs could be provided todocument breathing characteristics of the patient and to demonstrateimprovement to the patient over time.

In addition, the characteristic shape of the bioimpedance expansioncurve is an indicator of the success of each individual cough.Appropriate curves for maximizing exudate removal can be identified andthe adjustment levels of the Cough assist System (frequency, intensity,length of therapy, inhalation pressure, and exhalation pressure) can beadjusted to get the desired cough expulsion curve. Characteristics ofthe cough assist can be adjusted to ensure the optimal results areprovided for each individual patient.

Device and Method for Clinical Evaluation

Step 1: Most studies look at events over a given time period in total,i.e. events over a hospitalization or over 30 days and then look at whatinfluences that overall event rate. It is important to look more closelyto minimize the number of patients needed to study to get a meaningfulresult.

Step 2: To use a measurement or a group of measurements collected tostratify patients into who will have an event after the diagnosis orstratification is made, (diagnostic or stratification event) theninstead of looking at an extended period of time, looking at smalldefined time segments can be helpful in understanding the impact of theevent and importantly can help understand if the “event” is real or not.

In one embodiment, this is described as events/hour for every hour afterthe stratification event. Understanding the time interval for analysiscan be decided based on standard medical/clinical principles or byquerying the data with machine learning (AI) or other computerizedtechniques to determine the best interval for such analysis. Theduration of the interval may also be varied according to time after adiagnostic or stratification event, either continuously ordiscontinuously. For example, if a patient has low blood pressure in theOR, the patient would have a higher likelihood of having positivetroponins postoperatively. However, the positive troponins would mostlybe soon after surgery and not 3 days later, as such positive troponinswould likely not be related to the hypotension in the OR. Therefore, itis desirable to look at positive troponins at each hour after theepisode of hypotension.

In another example, if a patient has low minute ventilation(hypoventilation, i.e. respiratory depression) the last half hour in therecovery room (PACU) then it is statistically more likely that thepatient will have low minute ventilation (LowMV) postoperatively. Insuch as case, the LowMV postop is most prominent in the first 12 hoursafter surgery in a group that had LowMV the last half hour in the PACU,with some increased events extending until 48 hours, and rare LowMVs ingroups that had no LowMV the last half hour of the PACU.

For pulse oximetry, the situation is different. For example, in a groupthat has LowMV the last half hour in the PACU, the rate of desaturation/hour occurs somewhat uniformly across the 48 hour period. Alternatively,for patients with no LowMV the last half hour in the PACU, there isalmost no desaturation until the second day with a maximum desaturationaround 30 hours. Because this seems unlikely, such a temporal divisionof the data in hourly increments over time after PACU discharge may beused to help validate or invalidate the recorded desaturation as beingtrue. It would be unlikely that 12% of all patients would havedesaturation at 30 hours. So the qualification of the desaturation as atrue alarm can take into account the time passed since PACU discharge.

Pairing the two measurements may also help qualify the “true alarm”status of pulse oximetry with minimal possibility for the development ofsignificant hypoxia over a short or moderate period of time (as opposedto baseline hypoxia from cardiac or pulmonary disease). It is highlyunlikely that a patient would have significant hypoxia without asignificant change in ventilation. If the patient has respiratorydepression and hypoventilation, then desaturation will likely followwith a speed commensurate to the degree of hypoventilation. If thepatient becomes hypoxic from a pulmonary embolus (very rapidly) or heartfailure (more slowly) then the patient will likely try to compensatewith hyperventilation. If the lungs are compliant, as in COPD, then thehyperventilation will likely be more related to increase in TV than RR.If the lungs are stiff, as with CHF, then the hyperventilation willlikely be more related to an increase in RR than TV.

Step 3: Consider on a per patient basis over time.

In one embodiment, preferably one calculates the number of patients withx events per hour, as opposed to events per hour, and then uses thecalculation either to qualify the data or to define the timeframe forpatients having events after the stratifying event. For example, ifthere were 5 events in hour 2 it could be 5 events for one patient or 1event for 5 patients, which would have a different impact for both theindividual patient and also the way the measurement itself might beevaluated or qualified.

Step 4: Consider the time frames before and after the time frame ofinterest.

In one embodiment, if there are events the contiguous hours beforeand/or after the hour of interest, it is preferably considered that thisis a prolonged episode versus if there are events in two separate hoursduring the overall course. Such consideration preferably has a differentimpact for both the individual patient and also the way the measurementitself might be evaluated or qualified. Understanding how long or howmany timeframes (or hours) might be considered could be chosen bylooking at the overall range of duration of an event. Understanding the“long duration” events and number of such events could be determinedeither by discontinuous clustering of time frames or by other means ofseparation. See, for example, the blobbiness index. The actual incidenceof long duration events could be then looked at in terms of time sincestratification event.

Step 5: The time course of each patient after the stratification eventacross the entire time frame could be used with the temporal componentof the course used as a separate variable to assist in both analysis ofthe individual patient and a way to better qualify or understand themeasurement itself.

Multiple variables and time of presentation of such variables from aninitial stratification or diagnostic event may help define “true” vs“false” measurement status. Continuous or intermittent measurements caninclude minute ventilation (mV, TV, RR), temperature (peripheral, core),arterial blood pressure (systolic, diastolic, mean), cardiac output(Heart rate, stroke volume) oxygen saturation (oxyhb, deoxyhb, pulserate), CO2 (end tidal, transcutaneous), EKG (heart rate, arrhythmia),and with a different time constant. The initial time point ofstratification or diagnosis could, for example, be determined bypresentation of a patient, change in patient location, values of anyphysiologic, clinical or laboratory parameter or test.

In some embodiments one looks at the variables as separate in certainmeasurements. Time and frequency domain analysis of these parameters'signals can be used to remove artifact and have more accuratemeasurements. Preferably, one looks at oxy and deoxy hemoglobinseparately instead of combined in an oxygen saturation (oxy/oxy +deoxy)measurement. This has been problematic in terms of total hemoglobin in agiven sample being detected and technologies that try to measure totalhemoglobin have had issues doing so. However, looking at trending andchanges in data has proven more useful. Evaluating the change of oxy anddeoxy hemoglobin with respect to each other, the ratio of change and/ortime frame in which the change occurs, will validate the “true” and“false” oxygen saturation and total hemoglobin measurements.

In qualifying any measurement as “true” preferably one looks at themeasurement in the context of patient condition in general and in termsof the timeframe after a stratifying event in particular. In oneembodiment, if one wants to qualify a pulse ox reading as true and notrelated to an artifact of motion or of lack of perfusion systemically ordue to pressure on the probe or other issues, then the amount of totalhemoglobin or using oxy and deoxyhemoglobin as independent variables candetermine the appropriateness of the measurement. Over time, the timecourse of the change in total hemoglobin, or oxy hemoglobin ordeoxyhemoglobin, either independently or in some combination, could helpqualify a measurement as true or provide a level of confidence aroundthe measurement. In another embodiment, or in combination with theabove, in qualifying any measurement as “true” we can use the change ora change in the change (acceleration, second derivative) or variabilityor change in variability. Other measurements such as a change in minuteventilation, tidal volume and/or respiratory rate or end tidal CO2 ortranscutaneous CO2 would be expected to be associated, either before orafter a changed in oxygen saturation, and the timing of the change inthe other parameters or the slope of the change or variability of thosemeasurements or other features of the change in those measurements couldbe used to qualify the “true” nature of the pulse oximeter data. Onewould expect gradual or acute respiratory depression to cause a decreasein minute ventilation, tidal volume and/or respiratory rate in advanceof pulse oximeter changes. One would expect a “true” change in oxygensaturation gradually with congestive heart failure or other diffusiongradient in the lungs from sepsis or renal failure or volume overload.This can be differentiated from other causes of hypoxia by any of thefollowing—increased RR vs TV, increase in MV and time course ofoccurrence, time frame of occurrence since diagnostic or stratifyingevent, etc.

One would expect a “true” change in oxygen saturation gradually withCOPD or other circumstance with compliant lungs with poor capacity tooxygenate to cause a decrease in minute ventilation, tidal volume and/orrespiratory rate in advance of pulse oximeter changes. One would expecta “true” change in oxygen saturation gradually with COPD decompensationor other similar pathology in the lungs with compliant lungs to bedifferentiated from other causes of hypoxia by any of thefollowing—increased TV vs RR, increase in MV and time course ofoccurrence, time frame of occurrence since diagnostic or stratifyingevent, etc.

A mixture of pathologies could lead to the above physiologicmeasurements that would carry features of both pathologies such asincreases in both TV and RR with hyperventilation (increase in MV).While such determinations might be made with an unsupervised algorithmor AI system, by segmenting time and important variables alongphysiologic or clinical lines, fewer data points are required toevaluate a measurement or the effects of a therapeutic or other factoron a measurement.

In another embodiment, the adequacy of a sepsis score or indicator couldbe better evaluated by looking at the time course of the presentation ofthe sepsis vs the time that the stratification or diagnosis occurred.For example, if sepsis was determined to develop 48 hours after a“score” then it was less likely to be related to the score than if itdeveloped 2 hours after the score.

Another embodiment of the invention is directed to systems and methodsthat diagnose and/or stratify patients by taking in data from variousphysiological parameters, demographics, clinical events, treatments, labtest results using supervised and unsupervised learning techniquesincluding but not limited to machine learning, Bayesian inferencenetworks, expert systems, principal component analysis, support vectormachines, time series forecasting and analysis, fractal analysis,statistical techniques, heuristic algorithms, deep temporal continuousand discontinuous clustering that integrates dimensionality reductionand temporal clustering into a single end-to-end learning framework,maximum margin temporal clustering, fuzzy logic, and neural networks.

Inputs to this system and method can include, but are not limited to:

-   -   Minute ventilation (MV) and/or % of predicted MV    -   Tidal volume (TV)    -   Respiratory rate (RR)    -   Ventilation flow-volume relationship    -   Peripheral blood oxygen saturation (SpO2, hemoglobin, including        oxyhemoglobin and deoxyhemoglobin)    -   Mixed venous oxygen saturation (SvO2)    -   End-tidal carbon dioxide (EtCO2)    -   Sublingual and/or transcutaneous CO2    -   Body temperature (including core and surface temperatures)    -   Cardiac Output (CO, including stroke volume and pulse rate)    -   Cardiac index    -   Perfusion    -   Blood pressure (BP, including both systolic, diastolic and mean)    -   Heart Rate (HR)    -   Electrocardiogram (ECG, heart rate variability, arrhythmia,        heart rate)    -   Renal fluid volumes and rates    -   Pain level scores and locations    -   Medication information    -   Patient location (acuity of care)    -   Present and prior patient diagnoses and stratification of risk        based on different clinical evaluation techniques    -   Temporal and ratiometric relationships of parameter measurements        with respect to patient locations    -   Variation and rate of variation of any of the above parameters        and/or their components    -   Temporal and ratiometric relationships of all of the above        parameters

A heuristic algorithm can be developed based on human knowledge,studying clinical data, interactions with clinical staff, and iterationbetween these steps to make decisions for diagnosis and stratificationof patients.

Another embodiment can be developed to learn from an existing databaseto build a-priori knowledge to make inference decisions to determine thestratification or diagnosis of the patient. The system will preferablycontinue to adapt its a-priori knowledge based on new patient data, forexample from sources listed herein, to continually update thestratification and diagnosis decisions of the patient. Sensitivityanalysis may be done to determine the inputs to the system to makedecisions for each diagnosis or stratification type. A complete set orsubset of inputs associated with each diagnosis or stratification typecan be used to train the system and also be used in real-time todiagnose or stratify patients.

Moreover, another system can be developed that is a combination of thepreviously mentioned heuristic algorithms and machine learning systemsin order to diagnose and stratify patients.

Decisions, diagnoses and stratifications include, but are not limitedto:

-   -   Sepsis    -   Congestive heart failure    -   Hypovolemia    -   Hypertension (systemic and/or pulmonary)    -   COPD (emphysema, fibrosis)    -   Pulmonary embolism    -   Renal failure    -   Respiratory depression    -   Respiratory failure    -   Hypoxia    -   Changes in metabolism    -   Stratification of risk to develop the above conditions    -   Stratification of acuity of care necessary for each patient

FIG. 45 depicts an example of a Bayesian inference system for a subsetof inputs and a subset of diagnoses.

In another embodiment, systems and methods that take in data in the formof various physiological parameters, demographics, clinical events,treatments, lab test results using supervised and unsupervised learningtechniques and methods (including but not limited to machine learning,Bayesian inference networks, expert systems, principal componentanalysis, support vector machine, time series forecasting and analysis,fractal analysis, statistical techniques, heuristic algorithms, deeptemporal continuous and discontinuous clustering that integratesdimensionality reduction and temporal clustering into a singleend-to-end learning framework, maximum margin temporal clustering, fuzzylogic, and neural networks) in order to qualify alarms from variousmonitoring technologies as “true” or “false”. Rather than reporting allalarms from each monitor, the system's smart algorithm can label alarmsas “false” so as to reduce the increasing number of false alarms soundedby individual monitors, while reinforcing “true” alarms using orthogonalmonitoring data to confirm status of alarms.

Inputs can include, but are not limited to:

-   -   Minute ventilation (MV) and/or % of predicted MV

Tidal volume (TV) Respiratory rate (RR) Ventilation flow-volumerelationship

-   -   Peripheral blood oxygen saturation (SpO2, hemoglobin, including        oxyhemoglobin and deoxyhemoglobin)    -   Mixed venous oxygen saturation (SvO2)    -   End-tidal carbon dioxide (EtCO2)    -   Sublingual and/or transcutaneous CO2    -   Body temperature (including core and surface temperatures)    -   Cardiac Output (CO, including stroke volume and pulse rate)    -   Cardiac index    -   Perfusion    -   Blood pressure (BP, including both systolic, diastolic and mean)    -   Heart Rate (HR)    -   Electrocardiogram (ECG, heart rate variability, arrhythmia,        heart rate)    -   Renal fluid volumes and rates    -   Pain level scores and locations    -   Medication    -   Patient location (acuity of care)    -   Present and prior patient diagnoses and stratification of risk        based on different clinical evaluation techniques    -   Temporal and ratiometric relationships of parameter measurements        with respect to patient locations    -   Variation and rate of variation of any of the above parameters        and/or their components    -   Temporal and ratiometric relationships of all of the above        parameters

Alarms can include but are not limited to:

-   -   High/Low minute ventilation as percentage of predicted MV    -   High/Low tidal volume    -   High/Low respiratory rate    -   Apnea    -   High/Low EtCO2    -   SpO2 desaturation, hemoglobin level    -   High/Low oxygen extraction    -   Respiratory depression and its progression    -   High/Low heart rate    -   High/Low cardiac output    -   High/Low cardiac index    -   Cardiac preload, afterload and contractility    -   High/Low blood pressure    -   Heart failure and its progression    -   Arrhythmia    -   Sudden cardiac arrest    -   Hypertension (systemic and/or pulmonary)    -   Changes in metabolism

In another embodiment the machine learning and a-priori knowledge ispreferably learned from a database by considering both number of eventsper hour per patient and per population. The system will preferablycontinue to adapt its learning based on the per hour event per patient.

Another embodiment is directed to, systems and/or methods thatpreferably evaluate components of certain physiological measurement “A”that may or may not consider other physiological measurements (B, C,etc) to validate measurement A. Time and frequency domain analysis ofthese parameters' signals can be used by the systems and methods toremove artifact and have more accurate measurements. For example, theoxy and deoxy hemoglobin signals are preferably analyzed separatelyinstead of combined in an oxygen saturation (oxy/oxy+deoxy) measurementand may or may not use one or multiple of the inputs into the system asdescribed in earlier embodiments to evaluate the validity of the oxygensaturation measurement. Evaluating the change of oxy and deoxyhemoglobin with respect to each other, the ratio of change and/or timeframe in which the change occurs will preferably validate the “true” and“false” oxygen saturation and total hemoglobin measurements. FIG. 46shows an illustration of a system and method that uses a subset ofinputs using oxyhemoglobin, deoxyhemoglobin, and ECG to determine thevalidity of an oxygen saturation measurement and alarms.

Other embodiments and technical advantages of the invention are setforth herein and may be apparent from the drawings and the descriptionof the invention, or may be learned from the practice of the invention.The various embodiments described herein can be combined. Furthermore,elements from one embodiment may be used in another embodiment.

Other embodiments and uses of the invention will be apparent to thoseskilled in the art from consideration of the specification and practiceof the invention disclosed herein. All references cited herein,including all publications, U.S. and foreign patents and patentapplications, are specifically and entirely incorporated by reference.The term comprising, where ever used, is intended to include the termsconsisting and consisting essentially of. Furthermore, the termscomprising, including, and containing are not intended to be limiting.It is intended that the specification and examples be consideredexemplary only with the true scope and spirit of the invention indicatedby the following claims.

1. A method of evaluating a patient, comprising the steps of: obtainingtemporal data of the patient after an event; monitoring a plurality ofpatient parameters; compiling patient data based on the temporal dataand patient parameters; determining a state or change in state of thepatient based on the compiled patient data; and alerting medical staffof the state or change in state.
 2. The method of claim 1, wherein thetemporal data is used for diagnosing and/or stratifying the patientbased on the state of the patient and alerting medical staff of adiagnosis and/or stratification.
 3. The method of claim 2, wherein thetemporal data is used to qualify or weigh the data used in making thedetermination of patient state or in making a determination of adiagnosis and/or a stratification determination based on at least one ofthe amount of time after the event or the length of time elapsedmonitoring the patient.
 4. The method of claim 1, wherein an event is atleast one of a surgery, medication administration, intubation,extubation, lab tests, and a medical procedures, and patient careaction.
 5. The method of claim 1, wherein the parameters are at leastone of physiological parameters, demographics, location, clinicalevents, treatments, physical state, and lab test results.
 6. The methodof claim 1, wherein the patient parameters are obtained from sensors orinputs that measure Minute ventilation (MV) and/or % of predicted MV,Tidal volume (TV), Respiratory rate (RR), Ventilation flow-volumerelationship, Peripheral blood oxygen saturation (SpO2), Mixed venousoxygen saturation (SvO2), End-tidal carbon dioxide (EtCO2), Sublingualand/or transcutaneous CO2, temperature, Cardiac Output (CO), Cardiacindex, Perfusion, Blood pressure (BP), Heart Rate (HR),Electrocardiogram (ECG), Renal fluid volumes and rates, Pain levelscores and locations, Medication information, Patient location, Presentand prior patient diagnoses and stratification of risk based ondifferent clinical evaluation techniques, Temporal and ratiometricrelationships of parameter measurements with respect to patientlocations, Variation and rate of variation thereof, or Temporal andratiometric relationships thereof.
 7. The method of claim 2, wherein thediagnosing and/or stratifying of the patient is based on at least one ofsupervised and unsupervised learning techniques, machine learning,Bayesian inference networks, expert systems, principal componentanalysis, support vector machines, time series forecasting and analysis,fractal analysis, statistical techniques, heuristic algorithms, deeptemporal continuous and discontinuous clustering that integratesdimensionality reduction and temporal clustering into a singleend-to-end learning framework, maximum margin temporal clustering, fuzzylogic, and neural networks.
 8. The method of claim 2, wherein thediagnosis and/or stratifications are compared to a database of historicdiagnosis and/or stratifications.
 9. The method of claim 7, whereinfeedback from caregivers is updated in the database for future diagnosisand/or stratifications.
 10. The method of claim 1, wherein the patientparameters include independent measurements of tidal volume (TV) andrespiratory rate (RR) of the patient.
 11. The method of claim 9, whereinthe TV and RR are indicative of a patient's status.
 12. A system ofevaluating a patient, comprising: a processor adapted to obtain temporaldata of the patient after an event; a plurality of input devices incommunication with the processor adapted to monitor a plurality ofpatient parameters; wherein the processor: compiles patient dataincluding the temporal data and patient parameters; determines a stateor change in state of the patient based on the compiled patient data;and alerts medical staff of the state or change in state.
 13. The systemof claim 12, wherein the temporal data is used for diagnosing and/orstratifying the patient based on the state of the patient and alertingmedical staff of a diagnosis and/or stratification.
 14. The system ofclaim 13, wherein the temporal data is used to qualify or weigh the dataused in making the determination of patient state or in making adetermination of a diagnosis and/or a stratification determination basedon at least one of the amount of time after the event or the length oftime elapsed monitoring the patient.
 15. The system of claim 12, whereinan event is at least one of a surgery, medication administration,intubation, extubation, lab tests, and a medical procedures, and patientcare action.
 16. The system of claim 12, wherein the parameters are atleast one of physiological parameters, demographics, location, clinicalevents, treatments, physical state, and lab test results.
 17. The systemof claim 12, wherein the parameters are obtained from sensors or inputsthat measure Minute ventilation (MV) and/or % of predicted MV, Tidalvolume (TV), Respiratory rate (RR), Ventilation flow-volumerelationship, Peripheral blood oxygen saturation (SpO2), Mixed venousoxygen saturation (SvO2), End-tidal carbon dioxide (EtCO2), Sublingualand/or transcutaneous CO2, temperature, Cardiac Output (CO), Cardiacindex, Perfusion, Blood pressure (BP), Heart Rate (HR),Electrocardiogram (ECG), Renal fluid volumes and rates, Pain levelscores and locations, Medication information, Patient location, Presentand prior patient diagnoses and stratification of risk based ondifferent clinical evaluation techniques, Temporal and ratiometricrelationships of parameter measurements with respect to patientlocations, Variation and rate of variation thereof, or Temporal andratiometric relationships thereof.
 18. The system of claim 13, whereinthe diagnosing and/or stratifying of the patient is based on at leastone of supervised and unsupervised learning techniques, machinelearning, Bayesian inference networks, expert systems, principalcomponent analysis, support vector machines, time series forecasting andanalysis, fractal analysis, statistical techniques, heuristicalgorithms, deep temporal continuous and discontinuous clustering thatintegrates dimensionality reduction and temporal clustering into asingle end-to-end learning framework, maximum margin temporalclustering, fuzzy logic, and neural networks.
 19. The system of claim13, wherein the diagnosis and/or stratifications are compared to adatabase of historic diagnosis and/or stratifications.
 20. The system ofclaim 19, wherein feedback from caregivers is updated in the databasefor future diagnosis and/or stratifications.
 21. The system of claim 12,wherein the patient parameters include independent measurements of tidalvolume (TV) and respiratory rate (RR) of the patient.
 22. The system ofclaim 21, wherein the TV and RR are indicative of a patient's status.23. A method for evaluating a patient, comprising the steps of:obtaining tidal volume (TV) and respiratory rate (RR) of the patient;and diagnosing the patient based on independent measurements of TV andRR.
 24. The method of claim 23, wherein the TV and RR are obtained fromthe same sensor.
 25. The method of claim 24, wherein the sensor is arespiratory impedance sensor.
 26. The method of claim 23, furthercomprising providing an alert if the TV or RR is outside a predeterminedrange.
 27. The method of claim 23, wherein the TV and RR are independentvariables.
 28. The method of claim 23, wherein the method is used toevaluate a rapid shallow breathing index.
 29. The method of claim 23,wherein the TV and RR are indicative of a patient's status.
 30. A systemfor evaluating a patient, comprising: a sensor adapted to obtain tidalvolume (TV) and respiratory rate (RR) of the patient; and a processoradapted to diagnose the patient based on independent measurements of TVand RR.
 31. The system of claim 30, wherein TV and RR are obtained fromthe same sensor.
 32. The system of claim 30, wherein at least one sensoris a respiratory impedance sensor.
 33. The system of claim 30, whereinthe processor further provides an alert if TV or RR is outside apredetermined range.
 34. The system of claim 30, wherein the TV and RRare independent variables.
 35. The system of claim 30, wherein thesystem is used to evaluate a rapid shallow breathing index.
 36. Thesystem of claim 30, wherein the TV and RR is indicative of a patient'sstatus.
 37. A method of evaluating a patient, comprising the steps of:obtaining temporal data of the patient after an event; determining astate or change in state of the patient based on the temporal data; andalerting medical staff of the state or change in state.
 38. The methodof claim 37, wherein the temporal data is used for diagnosing and/orstratifying the patient based on the state of the patient and alertingmedical staff of a diagnosis and/or stratification.
 39. The method ofclaim 38, wherein the temporal data is used to weight the diagnosisand/or stratification determination based on at least one of the amountof time after the event or the length of time elapsed monitoring thepatient.
 40. The method of claim 37, wherein an event is at least one ofa surgery, medication administration, intubation, extubation, lab tests,and a medical procedures, and patient care action.
 41. The method ofclaim 38, wherein the diagnosing and/or stratifying of the patient isbased on at least one of supervised and unsupervised learningtechniques, machine learning, Bayesian inference networks, expertsystems, principal component analysis, support vector machines, timeseries forecasting and analysis, fractal analysis, statisticaltechniques, heuristic algorithms, deep temporal continuous anddiscontinuous clustering that integrates dimensionality reduction andtemporal clustering into a single end-to-end learning framework, maximummargin temporal clustering, fuzzy logic, and neural networks.
 42. Themethod of claim 38, wherein the diagnosis and/or stratifications arecompared to a database of historic diagnosis and/or stratifications. 43.The method of claim 42, wherein feedback from caregivers is updated inthe database for future diagnosis and/or stratifications.
 44. A systemof evaluating a patient, comprising: a processor adapted to: obtaintemporal data of the patient after an event; determine a state or changeof state of the patient the temporal data; and alert medical staff ofthe state or change in state.
 45. The system of claim 44, wherein thetemporal data is used for diagnosing and/or stratifying the patientbased on the state of the patient and alerting medical staff of adiagnosis and/or stratification.
 46. The system of claim 45, wherein thetemporal data is used to weight the diagnosis and/or stratificationdetermination based on at least one of the amount of time after theevent or the length of time elapsed monitoring the patient.
 47. Thesystem of claim 44, wherein an event is at least one of a surgery,medication administration, intubation, extubation, lab tests, and amedical procedures, and patient care action.
 48. The system of claim 44,wherein the diagnosing and/or stratifying of the patient is based on atleast one of supervised and unsupervised learning techniques, machinelearning, Bayesian inference networks, expert systems, principalcomponent analysis, support vector machines, time series forecasting andanalysis, fractal analysis, statistical techniques, heuristicalgorithms, deep temporal continuous and discontinuous clustering thatintegrates dimensionality reduction and temporal clustering into asingle end-to-end learning framework, maximum margin temporalclustering, fuzzy logic, and neural networks.
 49. The system of claim44, wherein the diagnosis and/or stratifications are compared to adatabase of historic diagnosis and/or stratifications.
 50. The system ofclaim 49, wherein feedback from caregivers is updated in the databasefor future diagnosis and/or stratifications.